Circulating biomarkers

ABSTRACT

Biomarkers can be assessed for diagnostic, therapy-related or prognostic methods to identify phenotypes, such as a condition or disease, or the stage or progression of a disease. Circulating biomarkers can be detected and optionally used in profiling of physiological states or determining phenotypes. These include nucleic acids, protein, and circulating structures such as vesicles. Biomarkers can be assessed for diagnostic, prognostic or theranostic purposes, e.g., to select candidate treatment regimens for diseases, conditions, disease stages, and stages of a condition, and can also be used to determine treatment efficacy. Examples of useful circulating biomarkers include polypeptides, nucleic acids (e.g., DNA, mRNA, microRNA) and vesicles.

CROSS-REFERENCE

This application claims the benefit of U.S. Provisional Patent Application Nos. 61/446,313, filed Feb. 24, 2011; 61/501,680, filed Jun. 27, 2011; 61/471,417, filed Apr. 4, 2011; 61/523,763, filed Aug. 15, 2011; and 61/445,273, filed Feb. 22, 2011; all of which applications are incorporated herein by reference in their entirety.

This application is a continuation-in-part of International Patent Application PCT/US2011/048327, filed Aug. 18, 2011, which application claims the benefit of U.S. Provisional Patent Application Nos. 61/374,951, filed Aug. 18, 2010; 61/379,670, filed Sep. 2, 2010; 61/381,305, filed Sep. 9, 2010; 61/383,305, filed Sep. 15, 2010; 61/391,504, filed Oct. 8, 2010; 61/393,823, filed Oct. 15, 2010; 61/411,890, filed Nov. 9, 2010; 61/414,870, filed Nov. 17, 2010; 61/416,560, filed Nov. 23, 2010; 61/421,851, filed Dec. 10, 2010; 61/423,557, filed Dec. 15, 2010; 61/428,196, filed Dec. 29, 2010; all of which applications are incorporated herein by reference in their entirety.

This application is also a continuation-in-part of International Patent Application PCT/US2011/026750, filed Mar. 1, 2011, which application claims is a continuation-in-part application of U.S. patent application Ser. No. 12/591,226, filed Nov. 12, 2009, which claims the benefit of U.S. Provisional Application Nos. 61/114,045, filed Nov. 12, 2008; 61/114,058, filed Nov. 12, 2008; 61/114,065, filed Nov. 13, 2008; 61/151,183, filed Feb. 9, 2009; 61/278,049, filed Oct. 2, 2009; 61/250,454, filed Oct. 9, 2009; and 61/253,027 filed Oct. 19, 2009; and which application also claims the benefit of U.S. Provisional Application Nos. 61/274,124, filed Mar. 1, 2010; 61/357,517, filed Jun. 22, 2010; 61/364,785, filed Jul. 15, 2010; all of which applications are incorporated herein by reference in their entirety.

This application is also a continuation-in-part of International Patent Application PCT/US2011/031479, filed Apr. 6, 2011, which application claims the benefit of U.S. Provisional Patent Application Nos. 61/321,392, filed Apr. 6, 2010; 61/321,407, filed Apr. 6, 2010; 61/332,174, filed May 6, 2010; 61/348,214, filed May 25, 2010, 61/348,685, filed May 26, 2010; 61/354,125, filed Jun. 11, 2010; 61/355,387, filed Jun. 16, 2010; 61/356,974, filed Jun. 21, 2010; 61/357,517, filed Jun. 22, 2010; 61/362,674, filed Jul. 8, 2010; 61/413,377, filed Nov. 12, 2010; 61/322,690, filed Apr. 9, 2010; 61/334,547, filed May 13, 2010; 61/364,785, filed Jul. 15, 2010; 61/370,088, filed Aug. 2, 2010; 61/379,670, filed Sep. 2, 2010; 61/381,305, filed Sep. 9, 2010; 61/383,305, filed Sep. 15, 2010; 61/391,504, filed Oct. 8, 2010; 61/393,823, filed Oct. 15, 2010; 61/411,890, filed Nov. 9, 2010; and 61/416,560, filed Nov. 23, 2010; all of which applications are incorporated herein by reference in their entirety.

BACKGROUND

Biomarkers for conditions and diseases such as cancer include biological molecules such as proteins, peptides, lipids, RNAs, DNA and variations and modifications thereof.

The identification of specific biomarkers, such as DNA, RNA and proteins, can provide biosignatures that are used for the diagnosis, prognosis, or theranosis of conditions or diseases. Biomarkers can be detected in bodily fluids, including circulating DNA, RNA, proteins, and vesicles. Circulating biomarkers include proteins such as PSA and CA125, and nucleic acids such as SEPT9 DNA and PCA3 messenger RNA (mRNA). Circulating biomarkers also include circulating vesicles. Vesicles are membrane encapsulated structures that are shed from cells and have been found in a number of bodily fluids, including blood, plasma, serum, breast milk, ascites, bronchoalveolar lavage fluid and urine. Vesicles can take part in the communication between cells as transport vehicles for proteins, RNAs, DNAs, viruses, and prions. MicroRNAs are short RNAs that regulate the transcription and degradation of messenger RNAs. MicroRNAs have been found in bodily fluids and have been observed as a component within vesicles shed from tumor cells. The analysis of circulating biomarkers associated with diseases, including vesicles and/or microRNA, can aid in detection of disease or severity thereof, determining predisposition to a disease, as well as making treatment decisions.

Vesicles present in a biological sample provide a source of biomarkers, e.g., the markers are present within a vesicle (vesicle payload), or are present on the surface of a vesicle. Characteristics of vesicles (e.g., size, surface antigens, determination of cell-of-origin, payload) can also provide a diagnostic, prognostic or theranostic readout. There remains a need to identify biomarkers that can be used to detect and treat disease. microRNA and other biomarkers associated with vesicles as well as the characteristics of a vesicle can provide a diagnosis, prognosis, or theranosis.

The present invention provides methods and systems for characterizing a phenotype by detecting biomarkers that are indicative of disease or disease progress. The biomarkers can be circulating biomarkers including vesicles and microRNA.

SUMMARY

Disclosed herein are methods and compositions for characterizing a phenotype by analyzing a vesicle, such as a vesicle present in a biological sample derived from a subject's cell. Characterizing a phenotype for a subject or individual may include, but is not limited to, the diagnosis of a disease or condition, the prognosis of a disease or condition, the determination of a disease stage or a condition stage, a drug efficacy, a physiological condition, organ distress or organ rejection, disease or condition progression, therapy-related association to a disease or condition, or a specific physiological or biological state.

In an aspect, the invention provides a method of detecting one or more biomarker in a biological sample comprising: a) contacting a biological sample with a reagent designed to determine a presence or level of the one or more biomarker, wherein the one or more biomarker is selected from the biomarkers in any of FIGS. 1-60, or Tables 3-10, 12-17, 19-20, 22, 26, 28-50, 52, 54-64, 66, 67, 69-71, 73-85, 89-92, and a combination thereof; and b) identifying the one or more biomarkers in the biological sample, thereby detecting the one or more biomarker in the biological sample.

The biological sample may comprise a biological fluid. The biological fluid can include without limitation peripheral blood, sera, plasma, ascites, urine, cerebrospinal fluid (CSF), sputum, saliva, bone marrow, synovial fluid, aqueous humor, amniotic fluid, cerumen, breast milk, broncheoalveolar lavage fluid, semen, prostatic fluid, cowper's fluid or pre-ejaculatory fluid, female ejaculate, sweat, fecal matter, hair, tears, cyst fluid, pleural and peritoneal fluid, pericardial fluid, lymph, chyme, chyle, bile, interstitial fluid, menses, pus, sebum, vomit, vaginal secretions, mucosal secretion, stool water, pancreatic juice, lavage fluids from sinus cavities, bronchopulmonary aspirates, blastocyl cavity fluid, or umbilical cord blood. For example, the biological fluid can be blood, a blood derivative or a blood fraction, e.g., serum or plasma.

In embodiments of the methods of the invention, the biological sample comprises an extracellular microvesicle population. The microvesicle population can comprise microvesicles having a diameter between 10 nm and 1000 nm. For example, the microvesicle population can comprise microvesicles having a diameter between 20 nm and 200 nm, between 50-100 nm, between 100-1,000 nm, between 50-200 nm, between 50-80 nm, between 20-50 nm, or between 50-500 nm.

In some embodiments of the methods herein, the microvesicle population is isolated, in whole or in part, from the biological sample prior to the identifying step. Appropriate isolation techniques comprise size exclusion chromatography, density gradient centrifugation, differential centrifugation, nanomembrane ultrafiltration, immunoabsorbent capture, affinity selection, affinity purification, affinity capture, immunoassay, immunoprecipitation, microfluidic separation, flow cytometry or combinations thereof. Other isolation techniques that can be used are disclosed herein or known in the art.

The affinity selection may comprise contacting the microvesicle population with one or more binding agent (reagent). The one or more binding agent can be a nucleic acid, DNA molecule, RNA molecule, antibody, antibody fragment, aptamer, peptoid, zDNA, peptide nucleic acid (PNA), locked nucleic acid (LNA), lectin, peptide, dendrimer, membrane protein labeling agent, chemical compound, or a combination thereof. Other binding agents that can be used are disclosed herein or known in the art.

The one or more binding agent can be used to capture and/or detect the microvesicle population. The one or more binding agent can be an agent that specifically binds a microvesicle, e.g., a microvesicle surface marker. The surface marker can be selected from the group consisting of a tetraspanin, CD9, CD31, CD63, CD81, CD82, CD37, CD53, Rab-5b, Annexin V, MFG-E8, a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-17, 19-20, 22, 26, 28-50, 52, 54-64, 66, 67, 69-71, 73-85, 89-92, and a combination thereof.

In an embodiment, the one or more binding agent is bound to a substrate, including without limitation a well, a microbead and/or an array. The one or more binding agent can also carry a label such as described herein or known in the art, including without limitation a magnetic label, a fluorescent label, an enzymatic label, a radioisotope, a quantum dot, or a combination thereof.

In the methods of the invention, the one or more biomarker can be any useful biological entity that can be analyzed. In some embodiments, the one or more biomarker comprises a polypeptide or functional fragment thereof. In some embodiments, the one or more biomarker comprises a microvesicle surface antigen or functional fragment thereof. In still other embodiments, the one or more biomarker comprises a nucleic acid or functional fragment thereof. The nucleic acid can be without limitation DNA, RNA, mRNA, microRNA, or other small RNA found in the circulation and/or within vesicles. In some embodiment, the one or more biomarker comprises a plurality of types of biological entities. For example, the one or more biomarker can comprise a polypeptide and a nucleic acid molecule, or functional fragment of either.

As a non-limiting example, one embodiment of the invention comprises affinity selection of a microvesicle population using one or more binding agent to one or more microvesicle surface antigen, followed by assessment of nucleic acids and/or polypeptides found within the selected microvesicles.

In an embodiment of the methods of the invention, the one or more biomarker comprises a tetraspanin, e.g., CD9. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation prostate, lung, colon, breast, bladder, endometrial, liver, pancreatic, ovarian, esophageal or kidney cancer. The CD9 can be assessed to characterize a cancer.

In another embodiment of the methods of the invention, the one or more biomarker is selected from the group consisting of Gal3, BCA200, and a combination thereof. In another embodiment, the one or more biomarker is selected from the group consisting of OPN, NCAM, and a combination thereof. The one or more biomarker can be selected from the group consisting of Gal3, BCA200, OPN, NCAM, and a combination thereof. The one or more biomarker can be selected from the group consisting of Gal3 and/or BCA200, OPN and/or NCAM, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer. The one or more biomarker can be assessed to characterize a breast cancer.

The one or more biomarker can be selected from the group consisting of a tetraspanin, CD45, FasL, CTLA4, CD31, DLL4, VEGFR2, HIF2a, Tie2, Ang1, Muc1, CD147, TIMP1, TIMP2, MMP7, MMP9, and a combination thereof. The one or more biomarker can be selected from the group consisting of CD83 and FasL, CTLA4 and CD80, CD147 and TIMP1, TIMP2 and MMP9, HIF2a and Ang1, VEGFR2 and Tie2, CD45 and CTL4A, DLL4 and CD31, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer.

The one or more biomarker can be selected from the group consisting of 5T4 (trophoblast), ADAM10, AGER/RAGE, APC, APP (β-amyloid), ASPH (A-10), B7H3 (CD276), BACE1, BAI3, BRCA1, BDNF, BIRC2, C1GALT1, CA125 (MUC16), Calmodulin 1, CCL2 (MCP-1), CD9, CD10, CD127 (IL7R), CD174, CD24, CD44, CD63, CD81, CEA, CRMP-2, CXCR3, CXCR4, CXCR6, CYFRA 21, derlin 1, DLL4, DPP6, E-CAD, EpCaM, EphA2 (H-77), ER(1) ESR1 α, ER(2) ESR2 β, Erb B4, Erbb2, erb3 (Erb-B3) PA2G4, FRT (FLT1), Gal3, GPR30 (G-coupled ER1), HAP1, HER3, HSP-27, HSP70, IC3b, IL8, insig, junction plakoglobin, Keratin 15, KRAS, Mammaglobin, MART1, MCT2, MFGE8, MMP9, MRP8, Muc1, MUC17, MUC2, NCAM, NG2 (CSPG4), Nga1, NHE-3, NT5E (CD73), ODC1, OPG, OPN, p53, PARK7, PCSA, PGP9.5 (PARKS), PR(B), PSA, PSMA, RAGE, STXBP4, Survivin, TFF3 (secreted), TIMP1, TIMP2, TMEM211, TRAF4 (scaffolding), TRAIL-R2 (death Receptor 5), TrkB, Tsg 101, UNC93a, VEGF A, VEGFR2, YB-1, VEGFR1, GCDPF-15 (PIP), BigH3 (TGFb1-induced protein), 5HT2B (serotonin receptor 2B), BRCA2, BACE 1, CDH1-cadherin, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer. The one or more biomarker can be assessed to characterize a breast cancer.

In another embodiment, the one or more biomarker is selected from the group consisting of AK5.2, ATP6V1B1, CRABP1, and a combination thereof. The one or more biomarker can be selected from the group consisting of DST.3, GATA3, KRT81, and a combination thereof. The one or more biomarker can be selected from the group consisting of AK5.2, ATP6V1B1, CRABP1, DST.3, ELF5, GATA3, KRT81, LALBA, OXTR, RASL10A, SERHL, TFAP2A.1, TFAP2A.3, TFAP2C, VTCN1, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer. In an embodiment, one or more of the markers is assessed to characterize whether a cancer of unknown primary is derived from a breast cancer.

In some embodiment, the one or more biomarker is selected from the group consisting of a biomarker in Table 89, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer. In an embodiment, one or more of the markers is assessed to characterize a breast cancer. In another embodiment, the one or more biomarker is selected from the group consisting of a biomarker in Table 90, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer. In an embodiment, one or more of the markers is assessed to characterize a breast cancer, e.g., a ductal carcinoma in situ (DCIS). In still another embodiment, the one or more biomarker is selected from the group consisting of a biomarker in Table 91, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer. In an embodiment, one or more of the markers is assessed to characterize a breast cancer, e.g., to distinguish a DCIS or non-DCIS breast cancer.

The one or more biomarker assessed according to the methods of the invention can be selected from the group consisting of MS4A1, PRB, DR3, and a combination thereof. The one or more biomarker can also be selected from the group consisting of PRB, MACC1, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a lung cancer. In an embodiment, one or more of the markers is assessed to characterize a lung cancer.

In another embodiment of the methods of the invention, the one or more biomarker is selected from the group consisting of a biomarker in Table 92, and a combination thereof. In still another embodiment, the one or more biomarker comprises one or more microRNA selected from the group consisting of hsa-miR-125a-5p, hsa-miR-650, hsa-miR-194, hsa-miR-1200, hsa-miR-326, hsa-miR-30b*, hsa-miR-19a, hsa-miR-7a*, hsa-miR-708*, hsa-miR-99a, hsa-miR-199b-5p, hsa-miR-543, hsa-miR-71*, hsa-miR-518c*, hsa-miR-642, hsa-miR-654-3p, hsa-miR-518d-5p, hsa-miR-1266, hsa-miR-154, hsa-miR-662, hsa-miR-523, hsa-miR-198, hsa-miR-920, hsa-miR-885-3p, hsa-miR-99a*, hsa-miR-337-3p, hsa-miR-363, and a combination thereof. The one or more biomarker may also comprise miR-497 microRNA. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a lung cancer. In an embodiment, one or more of the markers is assessed to characterize a lung cancer.

In the methods above, the one or more biomarker can include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, or more of the listed biomarkers. The one or more biomarker can include all of the biomarkers above. The one or more biomarker may comprise any measurable biological entity, including without limitation a protein, a nucleic acid, or a combination thereof. For example, the one or more biomarker can be a peptide, polypeptide, protein, or fragment thereof. Alternately the one or more biomarker can be a nucleic acid such as DNA or RNA, including without limitation mRNA, microRNA, or fragments thereof. The one or more biomarker can also comprise a combination of biological entities, e.g., at least one protein and at least one nucleic acid.

In some embodiments of the methods of the invention, the microvesicle population is captured with the one or more binding agent to the one or more biomarker and is detected with a binding agent to a biomarker that is selected from the group consisting of a tetraspanin, CD9, CD31, CD63, CD81, CD82, CD37, CD53, Rab-5b, Annexin V, MFG-E8, a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-17, 19-20, 22, 26, 28-50, 52, 54-64, 66, 67, 69-71, 73-85, 89-92, and a combination thereof. For example, the one or more biomarker can include one or more of the biomarkers above.

Embodiments of the methods of the invention further comprise detecting the level of a payload within the microvesicle population. The detected payload can be any measureable biological entity within a vesicle, including without limitation one or more nucleic acid, peptide, protein, lipid, antigen, carbohydrate, and/or proteoglycan. The detected payload may comprise one or more biomarker selected from the group consisting of a biomarker above, or in any of FIGS. 1-60, or Tables 3-10, 12-14, 22, 26, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof. Nucleic acid biomarkers may comprise one or more DNA, mRNA, microRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, or shRNA. For example, the nucleic acid can include one or more microRNA above, or selected from the group consisting of microRNAs in any of Tables 5-9, 30-44, 58-59, 71 and 73. Nucleic acid biomarkers may also comprise one or more mRNA above, or selected from the group consisting of a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-17, 19-22, 22, 26, 28-29, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof. Protein biomarkers can comprise one or more peptide, polypeptide, protein or fragment thereof above, or selected from the group consisting of a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-17, 19-22, 22, 26, 28-29, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof.

The methods of the invention may further comprise assaying the biological sample for at least one additional biomarker that is selected from the group consisting of the biomarkers above, a tetraspanin, CD9, CD31, CD63, CD81, CD82, CD37, CD53, Rab-5b, Annexin V, MFG-E8, a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-14, 22, 26, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof. The one or more additional biomarker can be detected using any useful method comprised herein or known in the art.

As noted above, the biological sample may comprise a known or suspected cancer sample. In some embodiments, the biological sample comprises a cancer cell culture or a sample from a subject having or suspected of having the cancer. The cancer can be a cancer disclosed herein, including without limitation an acute lymphoblastic leukemia; acute myeloid leukemia; adrenocortical carcinoma; AIDS-related cancers; AIDS-related lymphoma; anal cancer; appendix cancer; astrocytomas; atypical teratoid/rhabdoid tumor; basal cell carcinoma; bladder cancer; brain stem glioma; brain tumor (including brain stem glioma, central nervous system atypical teratoid/rhabdoid tumor, central nervous system embryonal tumors, astrocytomas, craniopharyngioma, ependymoblastoma, ependymoma, medulloblastoma, medulloepithelioma, pineal parenchymal tumors of intermediate differentiation, supratentorial primitive neuroectodermal tumors and pineoblastoma); breast cancer; bronchial tumors; Burkitt lymphoma; cancer of unknown primary site; carcinoid tumor; carcinoma of unknown primary site; central nervous system atypical teratoid/rhabdoid tumor; central nervous system embryonal tumors; cervical cancer; childhood cancers; chordoma; chronic lymphocytic leukemia; chronic myelogenous leukemia; chronic myeloproliferative disorders; colon cancer; colorectal cancer; craniopharyngioma; cutaneous T-cell lymphoma; endocrine pancreas islet cell tumors; endometrial cancer; ependymoblastoma; ependymoma; esophageal cancer; esthesioneuroblastoma; Ewing sarcoma; extracranial germ cell tumor; extragonadal germ cell tumor; extrahepatic bile duct cancer; gallbladder cancer; gastric (stomach) cancer; gastrointestinal carcinoid tumor; gastrointestinal stromal cell tumor; gastrointestinal stromal tumor (GIST); gestational trophoblastic tumor; glioma; hairy cell leukemia; head and neck cancer; heart cancer; Hodgkin lymphoma; hypopharyngeal cancer; intraocular melanoma; islet cell tumors; Kaposi sarcoma; kidney cancer; Langerhans cell histiocytosis; laryngeal cancer; lip cancer; liver cancer; lung cancer; malignant fibrous histiocytoma bone cancer; medulloblastoma; medulloepithelioma; melanoma; Merkel cell carcinoma; Merkel cell skin carcinoma; mesothelioma; metastatic squamous neck cancer with occult primary; mouth cancer; multiple endocrine neoplasia syndromes; multiple myeloma; multiple myeloma/plasma cell neoplasm; mycosis fungoides; myelodysplastic syndromes; myeloproliferative neoplasms; nasal cavity cancer; nasopharyngeal cancer; neuroblastoma; Non-Hodgkin lymphoma; nonmelanoma skin cancer; non-small cell lung cancer; oral cancer; oral cavity cancer; oropharyngeal cancer; osteosarcoma; other brain and spinal cord tumors; ovarian cancer; ovarian epithelial cancer; ovarian germ cell tumor; ovarian low malignant potential tumor; pancreatic cancer; papillomatosis; paranasal sinus cancer; parathyroid cancer; pelvic cancer; penile cancer; pharyngeal cancer; pineal parenchymal tumors of intermediate differentiation; pineoblastoma; pituitary tumor; plasma cell neoplasm/multiple myeloma; pleuropulmonary blastoma; primary central nervous system (CNS) lymphoma; primary hepatocellular liver cancer; prostate cancer; rectal cancer; renal cancer; renal cell (kidney) cancer; renal cell cancer; respiratory tract cancer; retinoblastoma; rhabdomyosarcoma; salivary gland cancer; Sézary syndrome; small cell lung cancer; small intestine cancer; soft tissue sarcoma; squamous cell carcinoma; squamous neck cancer; stomach (gastric) cancer; supratentorial primitive neuroectodermal tumors; T-cell lymphoma; testicular cancer; throat cancer; thymic carcinoma; thymoma; thyroid cancer; transitional cell cancer; transitional cell cancer of the renal pelvis and ureter; trophoblastic tumor; ureter cancer; urethral cancer; uterine cancer; uterine sarcoma; vaginal cancer; vulvar cancer; Waldenström macroglobulinemia; or Wilm's tumor.

The methods above may further comprise comparing the presence or level of the one or more biomarker to a reference, wherein an altered presence or level relative to the reference provides a diagnostic, prognostic, or theranostic determination for the cancer. The diagnostic, prognostic, or theranostic determination for the cancer may comprise a diagnosis of the cancer or a likelihood of cancer, a prognosis of the cancer, a theranosis of the cancer, determining whether the cancer is responding to a therapeutic treatment, or determining whether the cancer is likely to respond to a therapeutic treatment. In embodiment, the therapeutic treatment is selected from Tables 10-13 or 69. The reference can be from a biological sample without the cancer. The reference can be from a series of biological samples measured at one or more different time point. In embodiments, elevated levels of the one or more biomarker in the sample as compared to the reference indicate the presence of or the likelihood of a cancer in the sample, or the presence of or the likelihood of a more advanced cancer in the sample.

In another aspect, the invention provides an assay comprising: a) isolating a extracellular microvesicle from a biological sample, wherein the microvesicle comprises one or more RNA molecule, wherein the one or more RNA molecule is a diagnostic indicator corresponding to a biomarker above or in any of FIGS. 1-60, or Tables 3-10, 12-17, 19-20, 22, 26, 28-50, 52, 54-64, 66, 67, 69-71, 73-85, 89-92; b) determining an amount of the one or more RNA molecule in the microvesicle; and c) comparing the determined amount of the one or more RNA molecule to one or more control level, wherein a cancer is detected if there is a difference in the amount of the one or more RNA molecule in the extracellular microvesicle as compared to the one or more control level. The isolating step can comprise a method disclosed herein or known in the art, e.g., size exclusion chromatography, density gradient centrifugation, differential centrifugation, nanomembrane ultrafiltration, immunoabsorbent capture, affinity selection, affinity purification, affinity capture, immunoassay, immunoprecipitation, microfluidic separation, flow cytometry or combinations thereof. In an embodiment, the affinity selection comprises contacting the microvesicle population with one or more binding agent that specifically binds a microvesicle surface marker selected from the biomarkers above, and/or a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-17, 19-22, 22, 26, 28-29, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof.

The methods above can be performed in vitro. In a related aspect, the invention provides use of one or more reagent to carry out the methods. Similarly, the invention provides a kit comprising one or more reagent to carry out the methods. The one or more reagent can comprise one or more binding agent to the one or more biomarker in the methods. The one or more reagent can also be one or more binding agent to one or more biomarker selected from the group consisting of a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-14, 22, 26, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof. In an embodiment, the one or more binding agent comprises an antibody or aptamer. The one or more binding agent can be tethered to a substrate. The one or more binding agent can be labeled. The one or more binding agent can comprise multiple binding agents in various forms, e.g., one or more binding agent can be tethered to a substrate and separately one or more labeled binding agent. The label can be any useful label described herein or known in the art, e.g., a magnetic label, a fluorescent label, an enzymatic label, a radioisotope, or a quantum dot.

In an aspect, the invention provides an isolated vesicle comprising one or more biomarker selected from the group consisting of the biomarkers listed in the methods above, and a combination thereof. In an embodiment, the vesicle comprises one or more biomarker selected from the group consisting of a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-14, 22, 26, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof.

INCORPORATION BY REFERENCE

All publications, patents and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

FIG. 1 (a)-(g) represents a table which lists exemplary cancers by lineage, group comparisons of cells/tissue, and specific disease states and antigens specific to those cancers, group cell/tissue comparisons and specific disease states. Furthermore, the antigen can be a biomarker. The one or more biomarkers can be altered relative to a reference, e.g., present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 2 (a)-(f) represents a table which lists exemplary cancers by lineage, group comparisons of cells/tissue, and specific disease states and binding agents specific to those cancers, group cell/tissue comparisons and specific disease states.

FIG. 3 (a)-(b) represents a table which lists exemplary breast cancer biomarkers that can be derived and analyzed from a vesicle specific to breast cancer to create a breast cancer specific vesicle biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 4 (a)-(b) represents a table which lists exemplary ovarian cancer biomarkers that can be derived from and analyzed from a vesicle specific to ovarian cancer to create an ovarian cancer specific biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 5 represents a table which lists exemplary lung cancer biomarkers that can be derived from and analyzed from a vesicle specific to lung cancer to create a lung cancer specific biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 6 (a)-(d) represents a table which lists exemplary colon cancer biomarkers that can be derived from and analyzed from a vesicle specific to colon cancer to create a colon cancer specific biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 7 represents a table which lists exemplary biomarkers specific to an adenoma versus a hyperplastic polyp that can be derived and analyzed from a vesicle specific to adenomas versus hyperplastic polyps. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 8 is a table which lists exemplary biomarkers specific to inflammatory bowel disease (IBD) versus normal tissue that can be derived and analyzed from a vesicle specific inflammatory bowel disease versus normal tissue. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 9( a)-(c) represents a table which lists exemplary biomarkers specific to an adenoma versus colorectal cancer (CRC) that can be derived and analyzed from a vesicle specific to adenomas versus colorectal cancer. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 10 represents a table which lists exemplary biomarkers specific to IBD versus CRC that can be derived and analyzed from a vesicle specific to IBD versus CRC. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 11 (a)-(b) represents a table which lists exemplary biomarkers specific to CRC Dukes B versus Dukes C-D that can be derived and analyzed from a vesicle specific to CRC Dukes B versus Dukes C-D. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 12( a)-(d) represents a table which lists exemplary biomarkers specific to an adenoma with low grade dysplasia versus an adenoma with high grade dysplasia that can be derived and analyzed from a vesicle specific to an adenoma with low grade dysplasia versus an adenoma with high grade dysplasia. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 13( a)-(b) represents a table which lists exemplary biomarkers specific to ulcerative colitis (UC) versus Crohn's Disease (CD) that can be derived and analyzed from a vesicle specific to UC versus CD. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 14 represents a table which lists exemplary biomarkers specific to a hyperplastic polyp versus normal tissue that can be derived and analyzed from a vesicle specific to a hyperplastic polyp versus normal tissue. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 15 is a table which lists exemplary biomarkers specific to an adenoma with low grade dysplasia versus normal tissue that can be derived and analyzed from a vesicle specific to an adenoma with low grade dysplasia versus normal tissue. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 16 is a table which lists exemplary biomarkers specific to an adenoma versus normal tissue that can be derived and analyzed from a vesicle specific to an adenoma versus normal tissue. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 17 represents a table which lists exemplary biomarkers specific to CRC versus normal tissue that can be derived and analyzed from a vesicle specific to CRC versus normal tissue. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 18 is a table which lists exemplary biomarkers specific to benign prostatic hyperplasia that can be derived from and analyzed from a vesicle specific to benign prostatic hyperplasia. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 19( a)-(c) represents a table which lists exemplary prostate cancer biomarkers that can be derived from and analyzed from a vesicle specific to prostate cancer to create a prostate cancer specific, biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 20( a)-(c) represents a table which lists exemplary melanoma biomarkers that can be derived from and analyzed from a vesicle specific to melanoma to create a melanoma specific biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 21( a)-(b) represents a table which lists exemplary pancreatic cancer biomarkers that can be derived from and analyzed from a vesicle specific to pancreatic cancer to create a pancreatic cancer specific biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 22 is a table which lists exemplary biomarkers specific to brain cancer that can be derived from and analyzed from a vesicle specific to brain cancer to create a brain cancer specific biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 23( a)-(b) represents a table which lists exemplary psoriasis biomarkers that can be derived from and analyzed from a vesicle specific to psoriasis to create a psoriasis specific biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 24( a)-(c) represents a table which lists exemplary cardiovascular disease biomarkers that can be derived from and analyzed from a vesicle specific to cardiovascular disease to create a cardiovascular disease specific biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 25 is a table which lists exemplary biomarkers specific to hematological malignancies that can be derived from and analyzed from a vesicle specific to hematological malignancies to create a specific biosignature for hematological malignancies. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 26( a)-(b) represents a table which lists exemplary biomarkers specific to B-Cell Chronic Lymphocytic Leukemias that can be derived from and analyzed from a vesicle specific to B-Cell Chronic Lymphocytic Leukemias to create a specific biosignature for B-Cell Chronic Lymphocytic Leukemias. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 27 is a table which lists exemplary biomarkers specific to B-Cell Lymphoma and B-Cell Lymphoma-DLBCL that can be derived from and analyzed from a vesicle specific to B-Cell Lymphoma and B-Cell Lymphoma-DLBCL. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 28 represents a table which lists exemplary biomarkers specific to B-Cell Lymphoma-DLBCL-germinal center-like and B-Cell Lymphoma-DLBCL-activated B-cell-like and B-cell lymphoma-DLBCL that can be derived from and analyzed from a vesicle specific to B-Cell Lymphoma-DLBCL-germinal center-like and B-Cell Lymphoma-DLBCL-activated B-cell-like and B-cell lymphoma-DLBCL. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 29 represents a table which lists exemplary Burkitt's lymphoma biomarkers that can be derived from and analyzed from a vesicle specific to Burkitt's lymphoma to create a Burkitt's lymphoma specific biosignature. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 30( a)-(b) represents a table which lists exemplary hepatocellular carcinoma biomarkers that can be derived from and analyzed from a vesicle specific to hepatocellular carcinoma to create a specific biosignature for hepatocellular carcinoma. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 31 is a table which lists exemplary biomarkers for cervical cancer that can be derived from and analyzed from a vesicle specific to cervical cancer. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 32 represents a table which lists exemplary biomarkers for endometrial cancer that can be derived from and analyzed from a vesicle specific to endometrial cancer to create a specific biosignature for endometrial cancer. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 33( a)-(b) represents a table which lists exemplary biomarkers for head and neck cancer that can be derived from and analyzed from a vesicle specific to head and neck cancer to create a specific biosignature for head and neck cancer. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 34 represents a table which lists exemplary biomarkers for inflammatory bowel disease (IBD) that can be derived from and analyzed from a vesicle specific to IBD to create a specific biosignature for IBD. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 35 is a table which lists exemplary biomarkers for diabetes that can be derived from and analyzed from a vesicle specific to diabetes to create a specific biosignature for diabetes. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 36 is a table which lists exemplary biomarkers for Barrett's Esophagus that can be derived from and analyzed from a vesicle specific to Barrett's Esophagus to create a specific biosignature for Barrett's Esophagus. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 37 is a table which lists exemplary biomarkers for fibromyalgia that can be derived from and analyzed from a vesicle specific to fibromyalgia. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 38 represents a table which lists exemplary biomarkers for stroke that can be derived from and analyzed from a vesicle specific to stroke to create a specific biosignature for stroke. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 39 is a table which lists exemplary biomarkers for Multiple Sclerosis (MS) that can be derived from and analyzed from a vesicle specific to MS to create a specific biosignature for MS. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 40( a)-(b) represents a table which lists exemplary biomarkers for Parkinson's Disease that can be derived from and analyzed from a vesicle specific to Parkinson's Disease to create a specific biosignature for Parkinson's Disease. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 41 represents a table which lists exemplary biomarkers for Rheumatic Disease that can be derived from and analyzed from a vesicle specific to Rheumatic Disease to create a specific biosignature for Rheumatic Disease. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 42( a)-(b) represents a table which lists exemplary biomarkers for Alzheimer's Disease that can be derived from and analyzed from a vesicle specific to Alzheimer's Disease to create a specific biosignature for Alzheimer's Disease. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 43 is a table which lists exemplary biomarkers for Prion Diseases that can be derived from and analyzed from a vesicle specific to Prion Diseases to create a specific biosignature for Prion Diseases. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 44 represents a table which lists exemplary biomarkers for sepsis that can be derived from and analyzed from a vesicle specific to sepsis to create a specific biosignature for sepsis. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 45 is a table which lists exemplary biomarkers for chronic neuropathic pain that can be derived from and analyzed from a vesicle specific to chronic neuropathic pain. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 46 is a table which lists exemplary biomarkers for peripheral neuropathic pain that can be derived from and analyzed from a vesicle specific to peripheral neuropathic pain. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 47 represents a table which lists exemplary biomarkers for Schizophrenia that can be derived from and analyzed from a vesicle specific to Schizophrenia to create a specific biosignature for Schizophrenia. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 48 is a table which lists exemplary biomarkers for bipolar disorder or disease that can be derived from and analyzed from a vesicle specific to bipolar disorder to create a specific biosignature for bipolar disorder. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 49 is a table which lists exemplary biomarkers for depression that can be derived from and analyzed from a vesicle specific to depression to create a specific biosignature for depression. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 50 is a table which lists exemplary biomarkers for gastrointestinal stromal tumor (GIST) that can be derived from and analyzed from a vesicle specific to GIST to create a specific biosignature for GIST. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 51( a)-(b) represent sa table which lists exemplary biomarkers for renal cell carcinoma (RCC) that can be derived from and analyzed from a vesicle specific to RCC to create a specific biosignature for RCC. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 52 is a table which lists exemplary biomarkers for cirrhosis that can be derived from and analyzed from a vesicle specific to cirrhosis to create a specific biosignature for cirrhosis. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 53 is a table which lists exemplary biomarkers for esophageal cancer that can be derived from and analyzed from a vesicle specific to esophageal cancer to create a specific biosignature for esophageal cancer. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 54 is a table which lists exemplary biomarkers for gastric cancer that can be derived from and analyzed from a vesicle specific to gastric cancer to create a specific biosignature for gastric cancer. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 55 is a table which lists exemplary biomarkers for autism that can be derived from and analyzed from a vesicle specific to autism to create a specific biosignature for autism. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 56 is a table which lists exemplary biomarkers for organ rejection that can be derived from and analyzed from a vesicle specific to organ rejection to create a specific biosignature for organ rejection. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 57 is a table which lists exemplary biomarkers for methicillin-resistant staphylococcus aureus that can be derived from and analyzed from a vesicle specific to methicillin-resistant staphylococcus aureus to create a specific biosignature for methicillin-resistant staphylococcus aureus. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 58 is a table which lists exemplary biomarkers for vulnerable plaque that can be derived from and analyzed from a vesicle specific to vulnerable plaque to create a specific biosignature for vulnerable plaque. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified, such as epigentically modified or post-translationally modified.

FIG. 59( a)-(i) is a table which lists exemplary gene fusions that can be derived from, or analyzed from a vesicle. The gene fusion can be biomarker, and can be present or absent, underexpressed or overexpressed, or modified, such as epigentically modified or post-translationally modified.

FIG. 60( a)-(b) is a table of genes and their associated miRNAs, of which the gene, such as the mRNA of the gene, their associated miRNAs, or any combination thereof, can be used as one or more biomarkers that can be analyzed from a vesicle. Furthermore, the one or more biomarkers can be present or absent, underexpressed or overexpressed, mutated, or modified.

FIG. 61A depicts a method of identifying a biosignature comprising nucleic acid to characterize a phenotype. FIG. 61B depicts a method of identifying a biosignature of a vesicle or vesicle population to characterize a phenotype.

FIG. 62 illustrates results obtained from screening for proteins on vesicles, which can be used as a biomarker for the vesicles. Antibodies to the proteins can be used as binding agents. Examples of proteins identified as a biomarker for a vesicle include Bcl-XL, ERCC1, Keratin 15, CD81/TAPA-1, CD9, Epithelial Specific Antigen (ESA), and Mast Cell Chymase. The biomarker can be present or absent, underexpressed or overexpressed, mutated, or modified in or on a vesicle and used in characterizing a condition.

FIG. 63 illustrates methods of characterizing a phenotype by assessing vesicle biosignatures. FIG. 63A is a schematic of a planar substrate coated with a capture antibody, which captures vesicles expressing that protein. The capture antibody is for a vesicle protein that is specific or not specific for vesicles derived from diseased cells (“disease vesicle”). The detection antibody binds to the captured vesicle and provides a fluorescent signal. The detection antibody can detect an antigen that is generally associated with vesicles, or is associated with a cell-of-origin or a disease, e.g., a cancer. FIG. 63B is a schematic of a bead coated with a capture antibody, which captures vesicles expressing that protein. The capture antibody is for a vesicle protein that is specific or not specific for vesicles derived from diseased cells (“disease vesicle”). The detection antibody binds to the captured vesicle and provides a fluorescent signal. The detection antibody can detect an antigen that is generally associated with vesicles, or is associated with a cell-of-origin or a disease, e.g., a cancer. FIG. 63C is an example of a screening scheme that can be performed by multiplexing using the beads as shown in FIG. 63B. FIG. 63D presents illustrative schemes for capturing and detecting vesicles to characterize a phenotype. FIG. 63E presents illustrative schemes for assessing vesicle payload to characterize a phenotype.

FIG. 64 is a schematic of protein expression patterns. Different proteins are typically not distributed evenly or uniformly on a vesicle shell. Vesicle-specific proteins are typically more common, while cancer-specific proteins are less common Capture of a vesicle can be more easily accomplished using a more common, less cancer-specific protein, and cancer-specific proteins used in the detection phase.

FIG. 65 illustrates a computer system that can be used in some exemplary embodiments of the invention.

FIGS. 66A-B depict scanning electron micrographs (SEMs) of EpCam conjugated beads that have been incubated with VCaP vesicles.

FIG. 67 illustrates a method of depicting results using a bead based method of detecting vesicles from a subject. FIG. 67A For an individual patient, a graph of the bead enumeration and signal intensity using a screening scheme as depicted in FIG. 63B, where ˜100 capture beads are used for each capture/detection combination assay per patient. For a given patient, the output shows number of beads detected vs. intensity of signal. The number of beads captured at a given intensity is an indication of how frequently a vesicle expresses the detection protein at that intensity. The more intense the signal for a given bead, the greater the expression of the detection protein. FIG. 67B is a normalized graph obtained by combining normal patients into one curve and cancer patients into another, and using bio-statistical analysis to differentiate the curves. Data from each individual is normalized to account for variation in the number of beads read by the detection machine, added together, and then normalized again to account for the different number of samples in each population.

FIG. 68 illustrates prostate cancer biosignatures. FIG. 68A is a histogram of intensity values collected from a multiplexing experiment using a microsphere platform, where beads were functionalized with CD63 antibody, incubated with vesicles purified from patient plasma, and then labeled with a phycoerythrin (PE) conjugated EpCam antibody. The darker shaded bars (blue) represent the population from 12 normal subjects and the lighter shaded bars (green) are from 7 stage 3 prostate cancer patients. FIG. 68B is a normalized graph for each of the histograms shown in FIG. 68A, as described in FIG. 67. The distributions are of a Gaussian fit to intensity values from the microsphere results of FIG. 68A for both prostate patient samples and normal samples. FIG. 68C is an example of one of the prostate biosignatures shown in FIG. 68B, the CD63 versus CD63 biosignature (upper graph) where CD63 is used as the detector and capture antibody. The lower three panels show the results of flow cytometry on three prostate cancer cell lines (VCaP, LNcap, and 22RV1). Points above the horizontal line indicate beads that captured vesicles with CD63 that contain B7H3. Beads to the right of the vertical line indicate beads that have captured vesicles with CD63 that have PSMA. Those beads that are above and to the right of the lines have all three antigens. CD63 is a surface protein that is associated with vesicles, PSMA is surface protein that is associated with prostate cells, and B7H3 is a surface protein that is associated with aggressive cancers (specifically prostate, ovarian, and non-small-cell lung). The combination of all three antigens together identifies vesicles that are from cancer prostate cells. The majority of CD63 expressing prostate cancer vesicles also have prostate-specific membrane antigen, PSMA, and B7H3 (implicated in regulation of tumor cell migration and invasion and an indicator of aggressive cancer as well as clinical outcome). FIG. 68D is a prostate cancer vesicle topography. The upper panels show the results of capturing and labeling with CD63, CD9, and CD81 in various combinations. Almost all points are in the upper right quadrant indicating that these three markers are highly coupled. The lower row depicts the results of capturing cell line vesicles with B7H3 and labeling with CD63 and PSMA. Both VCaP and 22RV1 show that most vesicles captured with B7H3 also have CD63, and that there are two populations, those with PSMA and those without. The presence of B7H3 may be an indication of how aggressive the cancer is, as LNcap does not have a high amount of B7H3 containing vesicles (not many spots with CD63). LnCap is an earlier stage prostate cancer analogue cell line.

FIG. 69 illustrates colon cancer biosignatures. (A) depicts histograms of intensity values collected from various multiplexing experiments using a microsphere platform, where beads were functionalized with a capture antibody, incubated with vesicles purified form patient plasma, and then labeled with a detector antibody. The darker shaded bars (blue) represent the population from normals and the lighter shaded bars (green) are from colon cancer patients. (B) shows a normalized graph for each of the histograms shown in (A). (C) depicts a histogram of intensity values collected from a multiplexing experiment where beads where functionalized with CD66 antibody (the capture antibody), incubated with vesicles purified from patient plasma, and then labeled with a PE conjugated EpCam antibody (the detector antibody). The red population is from 6 normals and the green is from 21 colon cancer patients. Data from each individual was normalized to account for variation in the number of beads detected, added together, and then normalized again to account for the different number of samples in each population.

FIG. 70 illustrates multiple detectors can increase the signal. (A) Median intensity values are plotted as a function of purified concentration from the VCaP cell line when labeled with a variety of prostate specific PE conjugated antibodies. Vesicles captured with EpCam (left graphs) or PCSA (right graphs) and the various proteins detected by the detector antibody are listed to the right of each graph. In both cases the combination of CD9 and CD63 gives the best increase in signal over background (bottom graphs depicting percent increase). The combination of CD9 and CD63 gave about 200% percent increase over background. (B) further illustrates prostate cancer/prostate vesicle-specific marker multiplexing improves detection of prostate cancer cell derived vesicles. Median intensity values are plotted as a function of purified concentration from the VCaP cell line when labeled with a variety of prostate specific PE conjugated antibodies. Vesicles captured with PCSA (left) and vesicles captured with EpCam (right) are depicted. In both cases the combination of B7H3 and PSMA gives the best increase in signal over background.

FIG. 71 illustrates a colon cancer biosignature for colon cancer by stage, using CD63 detector and CD63 capture. The histograms of intensities from vesicles captured with CD63 coated beads and labeled with CD63 conjugated PE. There are 6 patients in the control group (A), 4 in stage I (B), 5 in stage II (C), 8 in stage III (D), and 4 stage IV (E). Data from each individual was normalized to account for variation in the number of beads detected, added together, and then normalized again to account for the different number of samples in each population (F).

FIG. 72 illustrates colon cancer biosignature for colon cancer by stage, using EpCam detector and CD9 capture. The histograms of intensities are from vesicles captured with CD9 coated beads and labeled with EpCam. There are patients in the (A) control group, (B) stage I, (C) stage II, (D) stage III, and (E) stage IV. Data from each individual was normalized to account for variation in the number of beads detected, added together, and then normalized again to account for the different number of samples in each population (F).

FIG. 73 illustrates (A) the sensitivity and specificity, and the confidence level, for detecting prostate cancer using antibodies to the listed proteins listed as the detector and capture antibodies. CD63, CD9, and CD81 are general markers and EpCam is a cancer marker. The individual results are depicted in (B) for EpCam versus CD63, with 99% confidence, 100% (n=8) cancer patient samples were different from the Generalized Normal Distribution and with 99% confidence, 77% (n=10) normal patient samples were not different from the Generalized Normal Distribution; (C) for CD81 versus CD63, with 99% confidence, 90% (n=5) cancer patient samples were different from the Generalized Normal Distribution; with 99% confidence, 77% (n=10) normal patient samples were not different from the Generalized Normal Distribution; (D) for CD63 versus CD63, with 99% confidence, 60% (n=5) cancer patient samples were different from the Generalized Normal Distribution; with 99% confidence, 80% (n=10) normal patient samples were not different from the Generalized Normal Distribution; (E) for CD9 versus CD63, with 99% confidence, 90% (n=5) cancer patient samples were different from the Generalized Normal Distribution; with 99% confidence, 77% (n=10) normal patient samples were not different from the Generalized Normal Distribution.

FIG. 74 illustrates (A) the sensitivity and the confidence level for detecting colon cancer using antibodies to the listed proteins listed as the detector and capture antibodies. CD63, CD9 are general markers, EpCam is a cancer marker, and CD66 is a colon marker. The individual results are depicted in (B) for EpCam versus CD63, with 99% confidence, 95% (n=20) cancer patient samples were different from the Generalized Normal Distribution; with 99% confidence, 100% (n=6) normal patient samples were not different from the Generalized Normal Distribution; (C) for EpCam versus CD9, with 99% confidence, 90% (n=20) cancer patient samples were different from the Generalized Normal Distribution; with 99% confidence, 77% (n=6) normal patient samples were not different from the Generalized Normal Distribution; (D) for CD63 versus CD63, with 99% confidence, 60% (n=20) cancer patient samples were different from the Generalized Normal Distribution; with 99% confidence, 80% (n=6) normal patient samples were not different from the Generalized Normal Distribution; (E) for CD9 versus CD63, with 99% confidence, 90% (n=20) cancer patient samples were different from the Generalized Normal Distribution; with 99% confidence, 77% (n=6) normal patient samples were not different from the Generalized Normal Distribution; (F) for CD66 versus CD9, with 99% confidence, 90% (n=20) cancer patient samples were different from the Generalized Normal Distribution; with 99% confidence, 77% (n=6) normal patient samples were not different from the Generalized Normal Distribution.

FIG. 75 illustrates the capture of prostate cancer cells-derived vesicles from plasma with EpCam by assessing TMPRSS2-ERG expression. (A) Graduated amounts of VCAP purified vesicles were spiked into normal plasma. Vesicles were isolated using Dynal beads with either EPCAM antibody or its isotype control. RNA from the vesicles was isolated and the expression of the TMPRSS2:ERG fusion transcript was measured using qRT-PCR. (B) VCaP purified vesicles were spiked into normal plasma and then incubated with Dynal magnetic beads coated with either the EpCam or isotype control antibody. RNA was isolated directly from the Dynal beads. Equal volumes of RNA from each sample were used for RT-PCR and subsequent Taqman assays. (C) Cycle threshold (CT) differences of the SPINK1 and GAPDH transcripts between 22RV1 vesicles captured with EpCam and IgG2 isotype negative control beads. Higher CT values indicate lower transcript expression.

FIG. 76 illustrates the top ten differentially expressed microRNAs between VCaP prostate cancer cell derived vesicles and normal plasma vesicles. VCAP cell line vesicles and vesicles from normal plasma were isolated via ultracentrifugation followed by RNA isolation. MicroRNAs were profiled using qRT-PCR analysis. Prostate cancer cell line derived vesicles have higher levels (lower CT values) of the indicated microRNAs as depicted in the bar graph.

FIG. 77 depicts a bar graph of miR-21 expression with CD9 bead capture. 1 ml of plasma from prostate cancer patients, 250 ng/ml of LNCaP, or normal purified vesicles were incubated with CD9 coated Dynal beads. The RNA was isolated from the beads and the bead supernatant. One sample (#6) was also uncaptured for comparison. MiR-21 expression was measured with qRT-PCR and the mean CT values for each sample compared. CD9 capture improves the detection of miR-21 in prostate cancer samples.

FIG. 78 depicts a bar graph of miR-141 expression with CD9 bead capture. The experiment was performed as in FIG. 77, with miR-141 expression measured with qRT-PCR instead of miR-21.

FIG. 79 represents graphs showing detection of biomarkers CD9, CD81, and CD63 (A-D) or B7H3 and EpCam (E-H) with captures agents for CD9, CD63, CD81, PSMA, PCSA, B7H3, and EpCam for vesicles isolated from a sample (#126) using a 500 μl column with a 100 kDa MWCO (Millipore, Billerica, Mass.) (A, E), 7 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.) (B, F), 15 ml column with a 100 kDa MWCO (Millipore, Billerica, Mass.) (C, G), or 20 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.) (D, H).

FIG. 80 represents graphs showing detection of biomarkers CD9, CD81, and CD63 (A-D) or B7H3 and EpCam (E-H) with captures agents for CD9, CD63, CD81, PSMA, PCSA, B7H3, and EpCam for vesicles isolated from a sample (#342) using a 500 μl column with a 100 kDa MWCO (Millipore, Billerica, Mass.) (A, E), 7 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.) (B, F), 15 ml column with a 100 kDa MWCO (Millipore, Billerica, Mass.) (C, G), or 20 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.) (D, H).

FIG. 81 represents graphs showing detection of biomarkers CD9, CD81, and CD63 of vesicles with captures agents for CD9, CD63, CD81, PSMA, PCSA, B7H3, and EpCam from a sample (#126) (A-C) versus another sample (#117) (D-F) using a 7 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.) (A, D), 15 ml column with a 100 kDa MWCO (Millipore, Billerica, Mass.) (B, E), or 20 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.) (C, F).

FIG. 82 represents graphs showing detection of biomarkers CD9, CD63, and CD81 with the capture agent of A) CD9, B) PCSA, C) PSMA, and D) EpCam. The vesicles were isolated from control samples (healthy samples) and prostate cancer samples, Stage II prostate cancer (PCa) samples. There is improved separation between the PCa and controls with the column-based filtration method of isolation as compared to ultracentrifugation isolation of vesicles.

FIG. 83 depicts the comparison of the detection level of various biomarkers of vesicles isolated from a patient sample (#126) using ultracentrifugation versus a filter based method using a 500 μl column with a 100 kDa molecular weight cut off (MWCO) (Millipore, Billerica, Mass.). The graphs depict A) ultracentrifugation purified sample; B) Microcon sample C) ultracentrifugation purified sample and 10 ug Vcap and D) Microcon sample with 10 ug Vcap. The captures agents used are CD9, CD63, CD81, PSMA, PCSA, B7H3, and EpCam, and CD9, CD81, and CD 63 detected.

FIG. 84 depicts the comparison of the detection level of various biomarkers of vesicles isolated from a patient sample (#342) using ultracentrifugation versus a filter based method using a 500 μl column with a 100 kDa MWCO (Millipore, Billerica, Mass.). The graphs depict A) ultracentrifugation purified sample; B) Microcon sample C) ultracentrifugation purified sample and 10 ug Vcap and D) Microcon sample with 10 ug Vcap. The capture agents used are CD9, CD63, CD81, PSMA, PCSA, B7H3, and EpCam, and CD9, CD81, and CD 63 detected.

FIG. 85 illustrates separation and identification of vesicles using the MoFlo XDP.

FIGS. 86A-86D illustrate flow sorting of vesicles in plasma. FIG. 86A shows detection and sorting of PCSA positive vesicles in the plasma of prostate cancer patients. FIG. 86B shows detection and sorting of CD45 positive vesicles in the plasma of normal and prostate cancer patients. FIG. 86C shows detection and sorting of CD45 positive vesicles in the plasma of normal and breast cancer patients. FIG. 86D shows detection and sorting of DLL4 positive vesicles in the plasma of normal and prostate cancer patients.

FIG. 87 represents a schematic of detecting vesicles in a sample wherein the presence or level of the desired vesicles are assessed using a microsphere platform. FIG. 87A represents a schematic of isolating vesicles from plasma using a column based filtering method, wherein the isolated vesicles are subsequently assessed using a microsphere platform. FIG. 87B represents a schematic of compression of a membrane of a vesicle due to high-speed centrifugation, such as ultracentrifugation. FIG. 87C represents a schematic of detecting vesicles bound to microspheres using laser detection.

FIG. 88A illustrates the ability of a vesicle biosignature to discriminate between normal prostate and PCa samples. Cancer markers included EpCam and B7H3. General vesicle markers included CD9, CD81 and CD63. Prostate specific markers included PCSA. The test was found to be 98% sensitive and 95% specific for PCa vs normal samples. FIG. 88B illustrates mean fluorescence intensity (MFI) on the Y axis for vesicle markers of FIG. 88A in normal and prostate cancer patients.

FIG. 89A illustrates improved sensitivity of the vesicle assays of the invention versus conventional PCa testing. FIG. 89B illustrates improved specificity of the vesicle assays of the invention versus conventional PCa testing.

FIG. 90 illustrates discrimination of BPH samples from normals and PCa samples using CD63.

FIG. 91 illustrates the ability of a vesicle biosignature to discriminate between normal prostate and PCa samples. Cancer markers included EpCam and B7H3. General vesicle markers included CD9, CD81 and CD63. Prostate specific markers included PCSA. The test was found to be 98% sensitive and 84% specific for PCa vs normal & BPH samples.

FIG. 92 illustrates improved specificity of the vesicle assays of the invention for PCa versus conventional testing even when BPH samples are included.

FIG. 93 illustrates ROC curve analysis of the vesicle assays of the invention versus conventional testing.

FIG. 94 illustrates a correlation between general vesicle (e.g. vesicle “MV”) levels, levels of prostate-specific MVs and MVs with cancer markers.

FIG. 95 illustrates vesicle markers that distinguish between PCa and normal samples.

FIG. 96 is a schematic for A) a vesicle prostate cancer assay, which leads to a decision tree (B), C), D)) for determining whether a sample is positive for prostate cancer.

FIG. 97A shows the results of a vesicle detection assay for prostate cancer following the decision tree versus detection using elevated PSA levels. FIG. 97B shows the results of a vesicle detection assay for prostate cancer following the decision tree on a cohort of 933 PCa and non-PCa patient samples. FIG. 97C shows an ROC curve corresponding to the data shown in FIG. 97B.

FIG. 98 illustrates the use of cluster analysis to set the MFI threshold for vesicle biomarkers of prostate cancer. A) Raw and log transformed data for 149 samples. The raw data is plotted in the left column and the transformed data in the right. B) Cluster analysis on PSMA vs B7H3 using log transformed data as input. The circles (normals) and x's (cancer) show the two clusters found. The open large circles show the point that was used as the center of the cluster. Blue lines show the chosen cutoff for each parameter. C) Cluster analysis on PCSA vs B7H3 using log transformed data as input. The circles (normals) and x's (cancer) show the two clusters found. The open large circles show the point that was used as the center of the cluster. Blue lines show the chosen cutoff for each parameter. D) Cluster analysis on PSMA vs PCSA using log transformed data as input. The circles and x's show the two clusters found. The open large red circles show the point that was used as the center of the cluster. Blue lines show the chosen cutoff for each parameter. E) The thresholds determined in B-D) were applied to the larger set of data containing 313 samples, and resulted in a sensitivity of 92.8% and a specificity of 78.7%.

FIG. 99 illustrates mean fluorescence intensity (MFI) on the y-axis for assessing vesicles in prostate cancer (Cancer) and normal (Normal) samples. Vesicle protein biomarkers are indicated on the x-axis, including from left to right CD9, PSMA, PCSA, CD63, CD81, B7H3, IL-6, OPG-13 (also referred to as OPG), IL6R, PA2G4, EZH2, RUNX2, SERPINB3 and EpCam.

FIG. 100 illustrates differentiation of BPH vs stage III PCa using antibody arrays.

FIG. 101 illustrates levels of miR-145 in vesicles isolated from control and PCa samples.

FIGS. 102A-102B illustrate levels of miR-107 (FIG. 102A) and miR-574-3p (FIG. 102B) in vesicles isolated from control (non PCa) and prostate cancer samples, as indicated on the X axis. miRs were detected in isolated vesicles using Taqman assays. P values are shown below the plot. The Y axis shows copy number of miRs detected. In FIG. 102B, two outlier samples from each sample group with copy numbers well outside the deviation of the samples were excluded from analysis.

FIGS. 103A-103D illustrate levels of miR-141 (FIG. 103A), miR-375 (FIG. 103B), miR-200b (FIG. 103C) and miR-574-3p (FIG. 103D) in vesicles isolated from metastatic (M1) and non-metastatic (M0) prostate cancer samples. miRs were detected in isolated vesicles using Taqman assays.

FIGS. 104A-104B illustrate the use of miR-107 and miR-141 to identify false negatives from a vesicle-based diagnostic assay for prostate cancer. FIG. 104A illustrates a scheme for using miR analysis within vesicles to convert false negatives into true positives, thereby improving sensitivity. FIG. 104B illustrates a scheme for using miR analysis within vesicles to convert false positives into true negatives, thereby improving specificity. Normalized levels of miR-107 (FIG. 104C) and miR-141 (FIG. 104D) are shown on the Y axis for true positives (TP) called by the vesicle diagnostic assay, true negatives (TN) called by the vesicle diagnostic assay, false positives (FP) called by the vesicle diagnostic assay, and false negatives (FN) called by the vesicle diagnostic assay.

FIGS. 105A-105F illustrate box plots of the elevation of hsa-miR-432 (FIG. 105A), hsa-miR-143 (FIG. 105B), hsa-miR-424 (FIG. 105C), hsa-miR-204 (FIG. 105D), hsa-miR-581f (FIG. 105E) and hsa-miR-451 (FIG. 105F) in patients with or without PCa and PSA ≧ or <4.0 ng/ml. miRs were detected in isolated vesicles using Taqman assays. Levels of miRs detected by Taqman assays are displayed on the Y axis. The X axis shows four groups of samples. From left to right, “Control no” are control patients with PSA ≧4.0; “Control yes” are control patients with PSA <4.0; “Diseased no” are prostate cancer patients with PSA ≧4.0; and “Diseased yes” are prostate cancer patients with PSA <4.0.

FIG. 106 illustrates the levels of microRNAs miR-29a and miR-145 in vesicles isolated from plasma samples from prostate cancer (PCa) and controls.

FIG. 107 illustrates a plate layout for microbead assays.

FIGS. 108A-D illustrate the ability of various capture antibodies used to capture vesicles that distinguish colorectal cancer (CRC) versus normal samples. FIG. 108A illustrates a fold-change (Y-axis) in capture antibody antigens (X-axis) in CRC vesicle samples versus normals as measured by antibody array. FIG. 108B is similar except that the Y-axis represents the median fluorescence intensity (MFI) in CRC and normal samples as indicated by the legend. FIG. 108C is similar to FIG. 108B performed on an additional sample set.

FIG. 108D shows analysis using CD24 is used as a colon marker, TROP2 as a cancer marker, and the tetraspanins CD9, CD63 and CD81 as general vesicle markers.

FIGS. 109A-H illustrate detection of CRC in plasma samples by detecting vesicles using TMEM211 and/or CD24. FIG. 109A illustrates ROC curve analysis of the vesicle assays of the invention with the biomarker TMEM211. FIG. 109B illustrates ROC curve analysis of the vesicle assays of the invention with the biomarker CD24. FIG. 109C illustrates analysis of the vesicle assays of the invention for normals, subjects with colorectal cancer (CRC), and confounders. FIG. 109D illustrates analysis of vesicle samples in a follow on study using biomarker TMEM211 for normals, subjects with colorectal cancer (CRC), and confounders. FIG. 109E illustrates ROC curve analysis of the vesicle assays of the invention with the biomarker TMEM211. FIG. 109F-109H illustrate the results from an additional study with an expanded patient cohort. In FIG. 109F, median fluorescence intensity (MFI) for TMEM211 is shown on the X axis and MFI for CD24 is shown on the Y axis. Results for TMEM211 and CD24 to distinguish various classes of samples individually are shown in FIG. 109G and FIG. 109H, respectively.

FIG. 110 illustrates TaqMan Low Density Array (TLDA) miRNA card comparison of colorectal cancer (CRC) cell lines versus normal vesicles. The CRC cell lines are indicated to the right of the plot. The Y-axis shows a fold-change in expression in the CRC cell lines compared to normal controls. The miRNAs surveyed are indicated on the X-axis, and from left to right are miR-548c-5p, miR-362-3p, miR-422a, miR-597, miR-429, miR-200a, and miR-200b. For each miR, the bars from left to right correspond to cell lines LOVO, HT29, SW260, COLO205, HCT116 and RKO. These miRNAs were not overexpressed in normal or melanoma cells.

FIG. 111A illustrates differentiation of normal and CRC samples using miR 92 and miR 491. FIG. 111B illustrates differentiation of normal and CRC samples using miR 92 and miR 21. FIG. 111C illustrates differentiation of normal and CRC samples using multiplexing with miR 92, miR 21, miR 9 and miR 491.

FIG. 112 illustrates KRAS sequencing in a colorectal cancer (CRC) cell line and patient sample. Samples comprise genomic DNA obtained from the cell line (B) or from a tissue sample from the patient (D), or cDNA obtained from RNA payload within vesicles shed from the cell line (A) or from a plasma sample from the patient (C).

FIG. 113 illustrates discrimination of CRC by detecting TMEM211 and MUC1 in microvesicles from plasma samples. The X axis (MUC1) and Y axis (TMEM211) correspond to the median fluorescence intensity (MFI) of the detected vesicles in the samples. The horizontal and vertical lines are the MFI threshold values for detecting CRC for TMEM211 and MUC1, respectively.

FIG. 114A illustrates a graph depicting the fold change over normal of biomarkers detected in breast cancer patient samples (n=10) or normal controls (i.e., no breast cancer). Vesicles in plasma samples were captured with antibodies to the indicated antigens tethered to beads. The captured vesicles were detected with labeled antibodies to tetraspanins CD9, CD63 and CD81. The fold change on the Y axis is the fold change median fluorescence intensity (MFI) of the vesicles detected in the breast cancer samples compared to normal. FIG. 114B illustrates the level of various biomarkers detected in vesicles derived from breast cancer cell lines MCF7, T47D and MDA. T47D and MDA are metastatic cell lines.

FIG. 115A illustrates a fold-change in various biomarkers in membrane vesicle from lung cancer samples as compared to normal samples detected using antibodies against the indicated vesicle antigens. Black bars are the ratios of lung cancer samples to normal samples. White bars are the ratios of non-lung cancer samples to normal samples. The underlying data is presented in FIG. 115B. FIG. 115B illustrates fluorescence levels of membrane vesicles detected using antibodies against the indicated vesicle antigens. Fluorescence levels are averages from the following samples: normals (white), non-lung cancer samples (grey) and staged lung cancer samples (black). FIG. 115C shows the median fluorescence intensity (MFI) of vesicles detecting using EPHA2 (i), CD24 (ii), EGFR (iii), and CEA (iv) in samples from lung cancer patients and normal controls. FIG. 115D and FIG. 115E present plots of mean fluorescence intensity (MFI) on the Y axis for vesicles detected in samples from lung cancer and normal (non-lung cancer) subjects. Capture antibodies are indicated along the X axis. FIG. 115F shows a 3-dimensional plot for a three biomarker panel consisting of CENPH (vertical axis), PRO GRP (leftmost horizontal axis) and MMP9 (rightmost horizontal axis). Cancers are indicated on the plot by open rectangles and normals are indicated by closed triangles.

FIG. 116 presents a decision tree for detecting lung cancer using the indicated capture antibodies to detect vesicles.

FIG. 117A illustrates CD81 labeled vesicle level vs circulating tumor cells (CTCs) in plasma derived vesicles. Vesicles collected from patient (14 leftmost “CTC” samples) and normal plasma (four rightmost samples) had vesicle levels measured with CD81 and CTCs counted. FIG. 117B illustrates miR-21 copy number vs CTCs in EpCAM+ plasma derived vesicles. Patient samples (15 leftmost “CTC” samples) and normal samples (seven rightmost “Normal” samples) are indicated. Copy number was assessed by qRT-PCR of miR-21 from RNA extracted from EpCAM+ plasma derived vesicles. CTC counts were obtained from the same samples.

FIGS. 118A-118C illustrate the levels of vesicles in plasma from a breast cancer patient detected using antibodies to CD31 (FIG. 118A), DLL4 (FIG. 118B) and CD9 (FIG. 118C) after depletion of CD31+ positive vesicles from the sample.

FIG. 119 illustrates detection of Tissue Factor (TF) in vesicles from normal (non-cancer) plasma samples, breast cancer (BCa) plasma samples and prostate cancer (PCa) plasma samples. Vesicles in plasma samples were captured with anti-Tissue Factor antibodies tethered to microspheres. The captured vesicles were detected with labeled antibodies to tetraspanins CD9, CD63 and CD81.

FIG. 120 shows flow sorting of vesicles labeled with FITC-conjugated antibodies to the indicated vesicle antigens. (A) CD9/CD63 FITC-labeled vesicles from a colorectal cancer (CRC) patient and normal without CRC are gated for CD31 and DLL4 levels. (B) CD9/CD63 FITC-labeled vesicles from a normal and CRC patient are gated for TMEM211 and DLL4 levels. (C) CD9 FITC-labeled vesicles from a normal and breast cancer patient are gated for CD31 and DLL4 levels.

FIG. 121 illustrates a graph depicting the levels of DLL4-captured circulating microvesicles (cMVs) in the plasma of normal individuals and individuals with various cancers. Vesicles in plasma samples were captured with anti-DLL4 antibodies tethered to microbeads. The captured vesicles were detected with labeled antibodies to tetraspanins CD9, CD63 and CD81. The median fluorescence intensity (MFI) of the vesicles is shown on the Y-axis. Sample groups are indicated on the X-axis, including from left to right: normal controls (“Normal”; i.e., non-cancer), breast cancer (“Breast”), lung cancer (“Lung”), prostate cancer (“Prostate”), colorectal cancer (“Colorectal”), renal cancer (“Renal”), ovarian cancer (“Ovarian”), and pancreatic cancer (“Pancreatic”).

FIGS. 122A-C illustrate the ability of microRNA miR-497 to distinguish between lung cancer and normal (non-lung cancer) samples in patient blood samples. The Y-axis shows copy number of miR-497 in 0.1 ml of sample. In FIG. 122A, the horizontal line indicates a copy number of 1154 copies. In FIG. 122B, the horizontal line indicates a copy number of 1356. FIG. 122C is a receiver operating characteristic (ROC) curve for distinguishing non-small cell lung cancer and normal plasma samples by examining levels of miR-497 in circulating microvesicles (cMV). The data corresponds to FIG. 122B.

FIGS. 123A and 123B illustrate detection of CD9 positive (CD9+) vesicles in a panel of cancers and non-cancer samples. The Y-axis shows mean fluorescence intensity (MFI) of vesicles captured with anti-CD9 antibodies and detected with labeled antibodies against CD9, CD63 and CD81. FIG. 123A shows a comparison of all cancers as a group versus non-cancers (Normal). FIG. 123B shows a comparison of separate cancers versus the non-cancers.

FIGS. 124A-124E illustrate distinguishing breast cancer using vesicle surface marker detection. The plots show median fluorescence values (MFIs) obtained by detecting vesicles with the indicated markers. The vertical and horizontal lines indicate the MFI cutoffs used to separate groups of samples for each marker, e.g., cancer from non-cancer. FIG. 124A illustrates distinguishing breast cancer between cancer and non-cancer patients using Gal3 and BCA200 with a first set of cutoffs. FIG. 124B illustrates distinguishing breast cancer between cancer and non-cancer patients using Gal3 and BCA200 with a second set of cutoffs. FIG. 124C illustrates detection of breast cancer using Gal3 and BCA200 with additional confounder samples. FIG. 124D illustrates distinguishing breast cancer between cancer and confounder patients using OPN and NCAM. FIG. 124E illustrates a two-step procedure for distinguishing breast cancer. First, Gal3 and BCA200 are used to distinguish the samples as shown in the leftmost plot. The samples in the quadrant marked “Positive” are then assessed using OPN and NCAM as shown in the rightmost plot to separate false positive confounder patients.

FIGS. 125A-125C show plots of FACS screening of cMVs in breast cancer and healthy patients. The markers used to stain the cMVs are indicated in the plots. FIG. 125A shows staining with immunosuppressive markers CD45 (y-axis) and CTL4A (x-axis). FIG. 125B shows staining with metastatic markers MMP-7 (y-axis) and TIMP-1 (x-axis). FIG. 125C shows staining with angiogenic markers CD31 (y-axis) and VEGFR2 (x-axis).

FIGS. 126A-126B illustrate classifying breast cancer and other cancers using DNA microarray expression data. Samples 1-30 are breast cancer samples. Sample 31-60 are cancers of non-breast origin. In FIG. 126A, a generalized LASSO regression was used to classify the samples. The three gene transcripts used to build the classifier model include DST.3, GATA3 and KRT81. In FIG. 126B, a Bayesian Ensemble approach was used to classify the samples. The fifteen gene transcripts used to build the model include AK5.2, ATP6V1B1, CRABP1, DST.3, ELF5, GATA3, KRT81, LALBA, OXTR, RASL10A, SERHL, TFAP2A.1, TFAP2A.3, TFAP2C and VTCN1.

DETAILED DESCRIPTION OF THE INVENTION

Disclosed herein are methods and systems for characterizing a phenotype of a biological sample, e.g., a sample from a cell culture, an organism, or a subject. The phenotype can be characterized by assessing one or more biomarkers. The biomarkers can be associated with a vesicle or vesicle population, either presented vesicle surface antigens or vesicle payload. As used herein, vesicle payload comprises entities encapsulated within a vesicle. Vesicle associated biomarkers can comprise both membrane bound and soluble biomarkers. The biomarkers can also be circulating biomarkers, such as microRNA or protein assessed in a bodily fluid. Unless otherwise specified, the terms “purified” or “isolated” as used herein in reference to vesicles or biomarker components mean partial or complete purification or isolation of such components from a cell or organism. Furthermore, unless otherwise specified, reference to vesicle isolation using a binding agent includes binding a vesicle with the binding agent whether or not such binding results in complete isolation of the vesicle apart from other biological entities in the starting material.

A method of characterizing a phenotype by analyzing a circulating biomarker, e.g., a nucleic acid biomarker, is depicted in scheme 6100A of FIG. 61A, as a non-limiting illustrative example. In a first step 6101, a biological sample is obtained, e.g., a bodily fluid, tissue sample or cell culture. Nucleic acids are isolated from the sample 6103. The nucleic acid can be DNA or RNA, e.g., microRNA. Assessment of such nucleic acids can provide a biosignature for a phenotype. By sampling the nucleic acids associated with target phenotype (e.g., disease versus healthy, pre- and post-treatment), one or more nucleic acid markers that are indicative of the phenotype can be determined. Various aspects of the present invention are directed to biosignatures determined by assessing one or more nucleic acid molecules (e.g., microRNA) present in the sample 6105, where the biosignature corresponds to a predetermined phenotype 6107. FIG. 61B illustrates a scheme 6100B of using vesicles to isolate the nucleic acid molecules. In one example, a biological sample is obtained 6102, and one or more vesicles, e.g., vesicles from a particular cell-of-origin and/or vesicles associated with a particular disease state, are isolated from the sample 6104. The vesicles are analyzed 6106 by characterizing surface antigens associated with the vesicles and/or determining the presence or levels of components present within the vesicles (“payload”). Unless specified otherwise, the term “antigen” as used herein refers generally to a biomarker that can be bound by a binding agent (also referred to as a binding reagent), whether the binding agent is an antibody, aptamer, lectin, or other binding agent for the biomarker and regardless of whether such biomarker illicits an immune response in a host. Vesicle payload may be protein, including peptides and polypeptides, and/or nucleic acids such as DNA and RNAs. RNA payload includes messenger RNA (mRNA) and microRNA (also referred to herein as miRNA or miR). A phenotype is characterized based on the biosignature of the vesicles 6108. In another illustrative method of the invention, schemes 6100A and 6100B are performed together to characterize a phenotype. In such a scheme, vesicles and nucleic acids, e.g., microRNA, are assessed, thereby characterizing the phenotype.

In a related aspect, methods are provided herein for the discovery of biomarkers comprising assessing vesicle surface markers or payload markers in one sample and comparing the markers to another sample. Markers that distinguish between the samples can be used as biomarkers according to the invention. Such samples can be from a subject or group of subjects. For example, the groups can be, e.g., known responders and non-responders to a given treatment for a given disease or disorder. Biomarkers discovered to distinguish the known responders and non-responders provide a biosignature of whether a subject is likely to respond to a treatment such as a therapeutic agent, e.g., a drug or biologic.

Phenotypes

Disclosed herein are products and processes for characterizing a phenotype of an individual by analyzing a vesicle such as a membrane vesicle. A phenotype can be any observable characteristic or trait of a subject, such as a disease or condition, a disease stage or condition stage, susceptibility to a disease or condition, prognosis of a disease stage or condition, a physiological state, or response to therapeutics. A phenotype can result from a subject's gene expression as well as the influence of environmental factors and the interactions between the two, as well as from epigenetic modifications to nucleic acid sequences.

A phenotype in a subject can be characterized by obtaining a biological sample from a subject and analyzing one or more vesicles from the sample. For example, characterizing a phenotype for a subject or individual may include detecting a disease or condition (including pre-symptomatic early stage detecting), determining the prognosis, diagnosis, or theranosis of a disease or condition, or determining the stage or progression of a disease or condition. Characterizing a phenotype can also include identifying appropriate treatments or treatment efficacy for specific diseases, conditions, disease stages and condition stages, predictions and likelihood analysis of disease progression, particularly disease recurrence, metastatic spread or disease relapse. A phenotype can also be a clinically distinct type or subtype of a condition or disease, such as a cancer or tumor. Phenotype determination can also be a determination of a physiological condition, or an assessment of organ distress or organ rejection, such as post-transplantation. The products and processes described herein allow assessment of a subject on an individual basis, which can provide benefits of more efficient and economical decisions in treatment.

In an aspect, the invention relates to the analysis of vesicles to provide a biosignature to predict whether a subject is likely to respond to a treatment for a disease or disorder. Characterizating a phenotype includes predicting the responder/non-responder status of the subject, wherein a responder responds to a treatment for a disease and a non-responder does not respond to the treatment. Vesicles can be analyzed in the subject and compared to vesicle analysis of previous subjects that were known to respond or not to a treatment. If the vesicle biosignature in a subject more closely aligns with that of previous subjects that were known to respond to the treatment, the subject can be characterized, or predicted, as a responder to the treatment. Similarly, if the vesicle biosignature in the subject more closely aligns with that of previous subjects that did not respond to the treatment, the subject can be characterized, or predicted as a non-responder to the treatment. The treatment can be for any appropriate disease, disorder or other condition. The method can be used in any disease setting where a vesicle biosignature that correlates with responder/non-responder status is known.

The term “phenotype” as used herein can mean any trait or characteristic that is attributed to a vesicle biosignature that is identified utilizing methods of the invention. For example, a phenotype can be the identification of a subject as likely to respond to a treatment, or more broadly, it can be a diagnostic, prognostic or theranostic determination based on a characterized biosignature for a sample obtained from a subject.

The term “detect” (including variations thereof, e.g., “detecting”) as used herein can mean determining the presence of or level of a candidate biomarker, e.g., a nucleic acid, polypeptide or functional fragment thereof, in a biological sample or series of a biological samples. In embodiment, the sample or samples are obtained from a subject in order to detect a condition or disease or detect likelihood of a condition or disease. The term “functional fragment(s)” in respect to a biomarker can mean a stretch or fragment of the biomarker that is identifiable and may be less than the whole or complete sequence but sufficient to detect whether the biomarker is present and/or level of the biomarker present. For example, a functional fragment can be a polypeptide fragment or nucleic acid molecule sequence that can be identified.

In some embodiments, the phenotype comprises a disease or condition such as those listed in Table 1. For example, the phenotype can comprise the presence of or likelihood of developing a tumor, neoplasm, or cancer. A cancer detected or assessed by products or processes described herein includes, but is not limited to, breast cancer, ovarian cancer, lung cancer, colon cancer, hyperplastic polyp, adenoma, colorectal cancer, high grade dysplasia, low grade dysplasia, prostatic hyperplasia, prostate cancer, melanoma, pancreatic cancer, brain cancer (such as a glioblastoma), hematological malignancy, hepatocellular carcinoma, cervical cancer, endometrial cancer, head and neck cancer, esophageal cancer, gastrointestinal stromal tumor (GIST), renal cell carcinoma (RCC) or gastric cancer. The colorectal cancer can be CRC Dukes B or Dukes C-D. The hematological malignancy can be B-Cell Chronic Lymphocytic Leukemia, B-Cell Lymphoma-DLBCL, B-Cell Lymphoma-DLBCL-germinal center-like, B-Cell Lymphoma-DLBCL-activated B-cell-like, and Burkitt's lymphoma.

The phenotype can be a premalignant condition, such as actinic keratosis, atrophic gastritis, leukoplakia, erythroplasia, Lymphomatoid Granulomatosis, preleukemia, fibrosis, cervical dysplasia, uterine cervical dysplasia, xeroderma pigmentosum, Barrett's Esophagus, colorectal polyp, or other abnormal tissue growth or lesion that is likely to develop into a malignant tumor. Transformative viral infections such as HIV and HPV also present phenotypes that can be assessed according to the invention.

The cancer characterized by the methods of the invention can comprise, without limitation, a carcinoma, a sarcoma, a lymphoma or leukemia, a germ cell tumor, a blastoma, or other cancers. Carcinomas include without limitation epithelial neoplasms, squamous cell neoplasms squamous cell carcinoma, basal cell neoplasms basal cell carcinoma, transitional cell papillomas and carcinomas, adenomas and adenocarcinomas (glands), adenoma, adenocarcinoma, linitis plastica insulinoma, glucagonoma, gastrinoma, vipoma, cholangiocarcinoma, hepatocellular carcinoma, adenoid cystic carcinoma, carcinoid tumor of appendix, prolactinoma, oncocytoma, hurthle cell adenoma, renal cell carcinoma, grawitz tumor, multiple endocrine adenomas, endometrioid adenoma, adnexal and skin appendage neoplasms, mucoepidermoid neoplasms, cystic, mucinous and serous neoplasms, cystadenoma, pseudomyxoma peritonei, ductal, lobular and medullary neoplasms, acinar cell neoplasms, complex epithelial neoplasms, warthin's tumor, thymoma, specialized gonadal neoplasms, sex cord stromal tumor, thecoma, granulosa cell tumor, arrhenoblastoma, sertoli leydig cell tumor, glomus tumors, paraganglioma, pheochromocytoma, glomus tumor, nevi and melanomas, melanocytic nevus, malignant melanoma, melanoma, nodular melanoma, dysplastic nevus, lentigo maligna melanoma, superficial spreading melanoma, and malignant acral lentiginous melanoma. Sarcoma includes without limitation Askin's tumor, botryodies, chondrosarcoma, Ewing's sarcoma, malignant hemangio endothelioma, malignant schwannoma, osteosarcoma, soft tissue sarcomas including: alveolar soft part sarcoma, angiosarcoma, cystosarcoma phyllodes, dermatofibrosarcoma, desmoid tumor, desmoplastic small round cell tumor, epithelioid sarcoma, extraskeletal chondrosarcoma, extraskeletal osteosarcoma, fibrosarcoma, hemangiopericytoma, hemangiosarcoma, kaposi's sarcoma, leiomyosarcoma, liposarcoma, lymphangiosarcoma, lymphosarcoma, malignant fibrous histiocytoma, neurofibrosarcoma, rhabdomyosarcoma, and synovialsarcoma. Lymphoma and leukemia include without limitation chronic lymphocytic leukemia/small lymphocytic lymphoma, B-cell prolymphocytic leukemia, lymphoplasmacytic lymphoma (such as waldenstrom macroglobulinemia), splenic marginal zone lymphoma, plasma cell myeloma, plasmacytoma, monoclonal immunoglobulin deposition diseases, heavy chain diseases, extranodal marginal zone B cell lymphoma, also called malt lymphoma, nodal marginal zone B cell lymphoma (nmzl), follicular lymphoma, mantle cell lymphoma, diffuse large B cell lymphoma, mediastinal (thymic) large B cell lymphoma, intravascular large B cell lymphoma, primary effusion lymphoma, burkitt lymphoma/leukemia, T cell prolymphocytic leukemia, T cell large granular lymphocytic leukemia, aggressive NK cell leukemia, adult T cell leukemia/lymphoma, extranodal NK/T cell lymphoma, nasal type, enteropathy-type T cell lymphoma, hepatosplenic T cell lymphoma, blastic NK cell lymphoma, mycosis fungoides/sezary syndrome, primary cutaneous CD30-positive T cell lymphoproliferative disorders, primary cutaneous anaplastic large cell lymphoma, lymphomatoid papulosis, angioimmunoblastic T cell lymphoma, peripheral T cell lymphoma, unspecified, anaplastic large cell lymphoma, classical hodgkin lymphomas (nodular sclerosis, mixed cellularity, lymphocyte-rich, lymphocyte depleted or not depleted), and nodular lymphocyte-predominant hodgkin lymphoma. Germ cell tumors include without limitation germinoma, dysgerminoma, seminoma, nongerminomatous germ cell tumor, embryonal carcinoma, endodermal sinus turmor, choriocarcinoma, teratoma, polyembryoma, and gonadoblastoma. Blastoma includes without limitation nephroblastoma, medulloblastoma, and retinoblastoma. Other cancers include without limitation labial carcinoma, larynx carcinoma, hypopharynx carcinoma, tongue carcinoma, salivary gland carcinoma, gastric carcinoma, adenocarcinoma, thyroid cancer (medullary and papillary thyroid carcinoma), renal carcinoma, kidney parenchyma carcinoma, cervix carcinoma, uterine corpus carcinoma, endometrium carcinoma, chorion carcinoma, testis carcinoma, urinary carcinoma, melanoma, brain tumors such as glioblastoma, astrocytoma, meningioma, medulloblastoma and peripheral neuroectodermal tumors, gall bladder carcinoma, bronchial carcinoma, multiple myeloma, basalioma, teratoma, retinoblastoma, choroidea melanoma, seminoma, rhabdomyosarcoma, craniopharyngeoma, osteosarcoma, chondrosarcoma, myosarcoma, liposarcoma, fibrosarcoma, Ewing sarcoma, and plasmocytoma.

In a further embodiment, the cancer under analysis may be a lung cancer including non-small cell lung cancer and small cell lung cancer (including small cell carcinoma (oat cell cancer), mixed small cell/large cell carcinoma, and combined small cell carcinoma), colon cancer, breast cancer, prostate cancer, liver cancer, pancreas cancer, brain cancer, kidney cancer, ovarian cancer, stomach cancer, skin cancer, bone cancer, gastric cancer, breast cancer, pancreatic cancer, glioma, glioblastoma, hepatocellular carcinoma, papillary renal carcinoma, head and neck squamous cell carcinoma, leukemia, lymphoma, myeloma, or a solid tumor.

In embodiments, the cancer comprises an acute lymphoblastic leukemia; acute myeloid leukemia; adrenocortical carcinoma; AIDS-related cancers; AIDS-related lymphoma; anal cancer; appendix cancer; astrocytomas; atypical teratoid/rhabdoid tumor; basal cell carcinoma; bladder cancer; brain stem glioma; brain tumor (including brain stem glioma, central nervous system atypical teratoid/rhabdoid tumor, central nervous system embryonal tumors, astrocytomas, craniopharyngioma, ependymoblastoma, ependymoma, medulloblastoma, medulloepithelioma, pineal parenchymal tumors of intermediate differentiation, supratentorial primitive neuroectodermal tumors and pineoblastoma); breast cancer; bronchial tumors; Burkitt lymphoma; cancer of unknown primary site; carcinoid tumor; carcinoma of unknown primary site; central nervous system atypical teratoid/rhabdoid tumor; central nervous system embryonal tumors; cervical cancer; childhood cancers; chordoma; chronic lymphocytic leukemia; chronic myelogenous leukemia; chronic myeloproliferative disorders; colon cancer; colorectal cancer; craniopharyngioma; cutaneous T-cell lymphoma; endocrine pancreas islet cell tumors; endometrial cancer; ependymoblastoma; ependymoma; esophageal cancer; esthesioneuroblastoma; Ewing sarcoma; extracranial germ cell tumor; extragonadal germ cell tumor; extrahepatic bile duct cancer; gallbladder cancer; gastric (stomach) cancer; gastrointestinal carcinoid tumor; gastrointestinal stromal cell tumor; gastrointestinal stromal tumor (GIST); gestational trophoblastic tumor; glioma; hairy cell leukemia; head and neck cancer; heart cancer; Hodgkin lymphoma; hypopharyngeal cancer; intraocular melanoma; islet cell tumors; Kaposi sarcoma; kidney cancer; Langerhans cell histiocytosis; laryngeal cancer; lip cancer; liver cancer; malignant fibrous histiocytoma bone cancer; medulloblastoma; medulloepithelioma; melanoma; Merkel cell carcinoma; Merkel cell skin carcinoma; mesothelioma; metastatic squamous neck cancer with occult primary; mouth cancer; multiple endocrine neoplasia syndromes; multiple myeloma; multiple myeloma/plasma cell neoplasm; mycosis fungoides; myelodysplastic syndromes; myeloproliferative neoplasms; nasal cavity cancer; nasopharyngeal cancer; neuroblastoma; Non-Hodgkin lymphoma; nonmelanoma skin cancer; non-small cell lung cancer; oral cancer; oral cavity cancer; oropharyngeal cancer; osteosarcoma; other brain and spinal cord tumors; ovarian cancer; ovarian epithelial cancer; ovarian germ cell tumor; ovarian low malignant potential tumor; pancreatic cancer; papillomatosis; paranasal sinus cancer; parathyroid cancer; pelvic cancer; penile cancer; pharyngeal cancer; pineal parenchymal tumors of intermediate differentiation; pineoblastoma; pituitary tumor; plasma cell neoplasm/multiple myeloma; pleuropulmonary blastoma; primary central nervous system (CNS) lymphoma; primary hepatocellular liver cancer; prostate cancer; rectal cancer; renal cancer; renal cell (kidney) cancer; renal cell cancer; respiratory tract cancer; retinoblastoma; rhabdomyosarcoma; salivary gland cancer; Sézary syndrome; small cell lung cancer; small intestine cancer; soft tissue sarcoma; squamous cell carcinoma; squamous neck cancer; stomach (gastric) cancer; supratentorial primitive neuroectodermal tumors; T-cell lymphoma; testicular cancer; throat cancer; thymic carcinoma; thymoma; thyroid cancer; transitional cell cancer; transitional cell cancer of the renal pelvis and ureter; trophoblastic tumor; ureter cancer; urethral cancer; uterine cancer; uterine sarcoma; vaginal cancer; vulvar cancer; Waldenström macroglobulinemia; or Wilm's tumor. The methods of the invention can be used to characterize these and other cancers. Thus, characterizing a phenotype can be providing a diagnosis, prognosis or theranosis of one of the cancers disclosed herein.

The phenotype can also be an inflammatory disease, immune disease, or autoimmune disease. For example, the disease may be inflammatory bowel disease (IBD), Crohn's disease (CD), ulcerative colitis (UC), pelvic inflammation, vasculitis, psoriasis, diabetes, autoimmune hepatitis, Multiple Sclerosis, Myasthenia Gravis, Type I diabetes, Rheumatoid Arthritis, Psoriasis, Systemic Lupus Erythematosis (SLE), Hashimoto's Thyroiditis, Grave's disease, Ankylosing Spondylitis Sjogrens Disease, CREST syndrome, Scleroderma, Rheumatic Disease, organ rejection, Primary Sclerosing Cholangitis, or sepsis.

The phenotype can also comprise a cardiovascular disease, such as atherosclerosis, congestive heart failure, vulnerable plaque, stroke, or ischemia. The cardiovascular disease or condition can be high blood pressure, stenosis, vessel occlusion or a thrombotic event.

The phenotype can also comprise a neurological disease, such as Multiple Sclerosis (MS), Parkinson's Disease (PD), Alzheimer's Disease (AD), schizophrenia, bipolar disorder, depression, autism, Prion Disease, Pick's disease, dementia, Huntington disease (HD), Down's syndrome, cerebrovascular disease, Rasmussen's encephalitis, viral meningitis, neurospsychiatric systemic lupus erythematosus (NPSLE), amyotrophic lateral sclerosis, Creutzfeldt-Jacob disease, Gerstmann-Straussler-Scheinker disease, transmissible spongiform encephalopathy, ischemic reperfusion damage (e.g. stroke), brain trauma, microbial infection, or chronic fatigue syndrome. The phenotype may also be a condition such as fibromyalgia, chronic neuropathic pain, or peripheral neuropathic pain.

The phenotype may also comprise an infectious disease, such as a bacterial, viral or yeast infection. For example, the disease or condition may be Whipple's Disease, Prion Disease, cirrhosis, methicillin-resistant staphylococcus aureus, HIV, hepatitis, syphilis, meningitis, malaria, tuberculosis, or influenza. Viral proteins, such as HIV or HCV-like particles can be assessed in a vesicle, to characterize a viral condition.

The phenotype can also comprise a perinatal or pregnancy related condition (e.g. preeclampsia or preterm birth), metabolic disease or condition, such as a metabolic disease or condition associated with iron metabolism. For example, hepcidin can be assayed in a vesicle to characterize an iron deficiency. The metabolic disease or condition can also be diabetes, inflammation, or a perinatal condition.

The methods of the invention can be used to characterize these and other diseases and disorders that can be assessed via biomarkers. Thus, characterizing a phenotype can be providing a diagnosis, prognosis or theranosis of one of the diseases and disorders disclosed herein.

Subject

One or more phenotypes of a subject can be determined by analyzing one or more vesicles, such as vesicles, in a biological sample obtained from the subject. A subject or patient can include, but is not limited to, mammals such as bovine, avian, canine, equine, feline, ovine, porcine, or primate animals (including humans and non-human primates). A subject can also include a mammal of importance due to being endangered, such as a Siberian tiger; or economic importance, such as an animal raised on a farm for consumption by humans, or an animal of social importance to humans, such as an animal kept as a pet or in a zoo. Examples of such animals include, but are not limited to, carnivores such as cats and dogs; swine including pigs, hogs and wild boars; ruminants or ungulates such as cattle, oxen, sheep, giraffes, deer, goats, bison, camels or horses. Also included are birds that are endangered or kept in zoos, as well as fowl and more particularly domesticated fowl, i.e. poultry, such as turkeys and chickens, ducks, geese, guinea fowl. Also included are domesticated swine and horses (including race horses). In addition, any animal species connected to commercial activities are also included such as those animals connected to agriculture and aquaculture and other activities in which disease monitoring, diagnosis, and therapy selection are routine practice in husbandry for economic productivity and/or safety of the food chain.

The subject can have a pre-existing disease or condition, such as cancer. Alternatively, the subject may not have any known pre-existing condition. The subject may also be non-responsive to an existing or past treatment, such as a treatment for cancer.

Samples

The biological sample obtained from the subject can be any bodily fluid. For example, the biological sample can be peripheral blood, sera, plasma, ascites, urine, cerebrospinal fluid (CSF), sputum, saliva, bone marrow, synovial fluid, aqueous humor, amniotic fluid, cerumen, breast milk, broncheoalveolar lavage fluid, semen (including prostatic fluid), Cowper's fluid or pre-ejaculatory fluid, female ejaculate, sweat, fecal matter, hair, tears, cyst fluid, pleural and peritoneal fluid, pericardial fluid, lymph, chyme, chyle, bile, interstitial fluid, menses, pus, sebum, vomit, vaginal secretions, mucosal secretion, stool water, pancreatic juice, lavage fluids from sinus cavities, bronchopulmonary aspirates or other lavage fluids. A biological sample may also include the blastocyl cavity, umbilical cord blood, or maternal circulation which may be of fetal or maternal origin. The biological sample may also be a tissue sample or biopsy from which vesicles and other circulating biomarkers may be obtained. For example, cells from the sample can be cultured and vesicles isolated from the culture (see for example, Example 1). In various embodiments, biomarkers or more particularly biosignatures disclosed herein can be assessed directly from such biological samples (e.g., identification of presence or levels of nucleic acid or polypeptide biomarkers or functional fragments thereof) utilizing various methods, such as extraction of nucleic acid molecules from blood, plasma, serum or any of the foregoing biological samples, use of protein or antibody arrays to identify polypeptide (or functional fragment) biomarker(s), as well as other array, sequencing, PCR and proteomic techniques known in the art for identification and assessment of nucleic acid and polypeptide molecules.

Table 1 lists illustrative examples of diseases, conditions, or biological states and a corresponding list of biological samples from which vesicles may be analyzed.

TABLE 1 Examples of Biological Samples for Analysis of Circulating Biomarkers Related to Various Diseases, Conditions, or Biological States Illustrative Disease, Condition or Biological State Illustrative Biological Samples Cancers/neoplasms affecting the following tissue Blood, serum, plasma, cerebrospinal fluid (CSF), types/bodily systems: breast, lung, ovarian, colon, urine, sputum, ascites, synovial fluid, semen, nipple rectal, prostate, pancreatic, brain, bone, connective aspirates, saliva, bronchoalveolar lavage fluid, tears, tissue, glands, skin, lymph, nervous system, endocrine, oropharyngeal washes, feces, peritoneal fluids, pleural germ cell, genitourinary, hematologic/blood, bone effusion, sweat, tears, aqueous humor, pericardial marrow, muscle, eye, esophageal, fat tissue, thyroid, fluid, lymph, chyme, chyle, bile, stool water, amniotic pituitary, spinal cord, bile duct, heart, gall bladder, fluid, breast milk, pancreatic juice, cerumen, Cowper's bladder, testes, cervical, endometrial, renal, ovarian, fluid or pre-ejaculatory fluid, female ejaculate, digestive/gastrointestinal, stomach, head and neck, interstitial fluid, menses, mucus, pus, sebum, vaginal liver, leukemia, respiratory/thorasic, cancers of lubrication, vomit unknown primary (CUP) Neurodegenerative/neurological disorders: Blood, serum, plasma, CSF, urine Parkinson's disease, Alzheimer's Disease and multiple sclerosis, Schizophrenia, and bipolar disorder, spasticity disorders, epilepsy Cardiovascular Disease: atherosclerosis, Blood, serum, plasma, CSF, urine cardiomyopathy, endocarditis, vunerable plaques, infection Stroke: ischemic, intracerebral hemorrhage, Blood, serum, plasma, CSF, urine subarachnoid hemorrhage, transient ischemic attacks (TIA) Pain disorders: peripheral neuropathic pain and Blood, serum, plasma, CSF, urine chronic neuropathic pain, and fibromyalgia, Autoimmune disease: systemic and localized diseases, Blood, serum, plasma, CSF, urine, synovial fluid rheumatic disease, Lupus, Sjogren's syndrome Digestive system abnormalities: Barrett's esophagus, Blood, serum, plasma, CSF, urine irritable bowel syndrome, ulcerative colitis, Crohn's disease, Diverticulosis and Diverticulitis, Celiac Disease Endocrine disorders: diabetes mellitus, various forms Blood, serum, plasma, CSF, urine of Thyroiditis,, adrenal disorders, pituitary disorders Diseases and disorders of the skin: psoriasis Blood, serum, plasma, CSF, urine, synovial fluid, tears Urological disorders: benign prostatic hypertrophy Blood, serum, plasma, urine (BPH), polycystic kidney disease, interstitial cystitis Hepatic disease/injury: Cirrhosis, induced Blood, serum, plasma, urine hepatotoxicity (due to exposure to natural or synthetic chemical sources) Kidney disease/injury: acute, sub-acute, chronic Blood, serum, plasma, urine conditions, Podocyte injury, focal segmental glomerulosclerosis Endometriosis Blood, serum, plasma, urine, vaginal fluids Osteoporosis Blood, serum, plasma, urine, synovial fluid Pancreatitis Blood, serum, plasma, urine, pancreatic juice Asthma Blood, serum, plasma, urine, sputum, bronchiolar lavage fluid Allergies Blood, serum, plasma, urine, sputum, bronchiolar lavage fluid Prion-related diseases Blood, serum, plasma, CSF, urine Viral Infections: HIV/AIDS Blood, serum, plasma, urine Sepsis Blood, serum, plasma, urine, tears, nasal lavage Organ rejection/transplantation Blood, serum, plasma, urine, various lavage fluids Differentiating conditions: adenoma versus Blood, serum, plasma, urine, sputum, feces, colonic hyperplastic polyp, irritable bowel syndrome (IBS) lavage fluid versus normal, classifying Dukes stages A, B, C, and/or D of colon cancer, adenoma with low-grade hyperplasia versus high-grade hyperplasia, adenoma versus normal, colorectal cancer versus normal, IBS versus. ulcerative colitis (UC) versus Crohn's disease (CD), Pregnancy related physiological states, conditions, or Maternal serum, plasma, amniotic fluid, cord blood affiliated diseases: genetic risk, adverse pregnancy outcomes

The methods of the invention can be used to characterize a phenotype using a blood sample or blood derivative. Blood derivatives include plasma and serum. Blood plasma is the liquid component of whole blood, and makes up approximately 55% of the total blood volume. It is composed primarily of water with small amounts of minerals, salts, ions, nutrients, and proteins in solution. In whole blood, red blood cells, leukocytes, and platelets are suspended within the plasma. Blood serum refers to blood plasma without fibrinogen or other clotting factors (i.e., whole blood minus both the cells and the clotting factors).

The biological sample may be obtained through a third party, such as a party not performing the analysis of the biomarkers, whether direct assessment of a biological sample or by profiling one or more vesicles obtained from the biological sample. For example, the sample may be obtained through a clinician, physician, or other health care manager of a subject from which the sample is derived. Alternatively, the biological sample may obtained by the same party analyzing the vesicle. In addition, biological samples be assayed, are archived (e.g., frozen) or ortherwise stored in under preservative conditions.

The volume of the biological sample used for analyzing a vesicle can be in the range of between 0.1-20 mL, such as less than about 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 or 0.1 mL. In some embodiments, the sample is about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.5, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 mL. In some embodiments, the sample is about 1,000, 900, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 75, 50, 25 or 10 μl. For example, a small volume sample could be obtained by a prick or swab.

A sample of bodily fluid can be used as a sample for characterizing a phenotype. For example, biomarkers in the sample can be assessed to provide a diagnosis, prognosis and/or theranosis of a disease. The biomarkers can be circulating biomarkers, such as circulating proteins or nucleic acids. The biomarkers can also be associated with a vesicle or vesicle population. Methods of the invention can be applied to assess one or more vesicles, as well as one or more different vesicle populations that may be present in a biological sample or in a subject. Analysis of one or more biomarkers in a biological sample can be used to determine whether an additional biological sample should be obtained for analysis. For example, analysis of one or more vesicles in a sample of bodily fluid can aid in determining whether a tissue biopsy should be obtained.

A sample from a patient can be collected under conditions that preserve the circulating biomarkers and other entities of interest contained therein for subsequent analysis. In an embodiment, the samples are processed using one or more of CellSave Preservative Tubes (Veridex, North Raritan, N.J.), PAXgene Blood DNA Tubes (QIAGEN GmbH, Germany), and RNAlater (QIAGEN GmbH, Germany).

CellSave Preservative Tubes (CellSave tubes) are sterile evacuated blood collection tubes. Each tube contains a solution that contains Na2EDTA and a cell preservative. The EDTA absorbs calcium ions, which can reduce or eliminate blood clotting. The preservative preserves the morphology and cell surface antigen expression of epithelial and other cells. The collection and processing can be performed as described in a protocol provided by the manufacturer. Each tube is evacuated to withdraw venous whole blood following standard phlebotomy procedures as known to those of skill in the art. CellSave tubes are disclosed in U.S. Pat. Nos. 5,466,574; 5,512,332; 5,597,531; 5,698,271; 5,985,153; 5,993,665; 6,120,856; 6,136,182; 6,365,362; 6,551,843; 6,620,627; 6,623,982; 6,645,731; 6,660,159; 6,790,366; 6,861,259; 6,890,426; 7,011,794; 7,282,350; 7,332,288; 5,849,517 and 5,459,073, each of which is incorporated by reference in its entirety herein.

The PAXgene Blood DNA Tube (PAXgene tube) is a plastic, evacuated tube for the collection of whole blood for the isolation of nucleic acids. The tubes can be used for blood collection, transport and storage of whole blood specimens and isolation of nucleic acids contained therein, e.g., DNA or RNA. Blood is collected under a standard phlebotomy protocol into an evacuated tube that contains an additive. The collection and processing can be performed as described in a protocol provided by the manufacturer. PAXgene tubes are disclosed in U.S. Pat. Nos. 5,906,744; 4,741,446; 4,991,104, each of which is incorporated by reference in its entirety herein.

The RNAlater RNA Stabilization Reagent (RNAlater) is used for immediate stabilization of RNA in tissues. RNA can be unstable in harvested samples. The aqueous RNAlater reagent permeates tissues and other biological samples, thereby stabilizing and protecting the RNA contained therein. Such protection helps ensure that downstream analyses reflect the expression profile of the RNA in the tissue or other sample. The samples are submerged in an appropriate volume of RNAlater reagent immediately after harvesting. The collection and processing can be performed as described in a protocol provided by the manufacturer. According to the manufacturer, the reagent preserves RNA for up to 1 day at 37° C., 7 days at 18-25° C., or 4 weeks at 2-8° C., allowing processing, transportation, storage, and shipping of samples without liquid nitrogen or dry ice. The samples can also be placed at −20° C. or −80° C., e.g., for archival storage. The preserved samples can be used to analyze any type of RNA, including without limitation total RNA, mRNA, and microRNA. RNAlater can also be useful for collecting samples for DNA, RNA and protein analysis. RNAlater is disclosed in U.S. Pat. No. 5,346,994, each of which is incorporated by reference in its entirety herein.

Vesicles

Methods of the invention can include assessing one or more vesicles, including assessing vesicle populations. A vesicle, as used herein, is a membrane vesicle that is shed from cells. Vesicles or membrane vesicles include without limitation: circulating microvesicles (cMVs), microvesicle, exosome, nanovesicle, dexosome, bleb, blebby, prostasome, microparticle, intralumenal vesicle, membrane fragment, intralumenal endosomal vesicle, endosomal-like vesicle, exocytosis vehicle, endosome vesicle, endosomal vesicle, apoptotic body, multivesicular body, secretory vesicle, phospholipid vesicle, liposomal vesicle, argosome, texasome, secresome, tolerosome, melanosome, oncosome, or exocytosed vehicle. Furthermore, although vesicles may be produced by different cellular processes, the methods of the invention are not limited to or reliant on any one mechanism, insofar as such vesicles are present in a biological sample and are capable of being characterized by the methods disclosed herein. Unless otherwise specified, whenever any of the methods and compositions herein refer to vesicles they also refer to any of the above species of vesicles. In addition, whenever any of the methods and compositions herein refers to a species of vesicle, it is understood that all other species of vesicles may also be used unless noted. Unless otherwise specified, methods that make use of a species of vesicle can be applied to other types of vesicles. Vesicles comprise spherical structures with a lipid bilayer similar to cell membranes which surrounds an inner compartment which can contain soluble components, sometimes referred to as the payload. In some embodiments, the methods of the invention make use of exosomes, which are small secreted vesicles of about 40-100 nm in diameter. For a review of membrane vesicles, including types and characterizations, see Thery et al., Nat Rev Immunol. 2009 August; 9(8):581-93. Some properties of different types of vesicles include those in Table 2:

TABLE 2 Vesicle Properties Membrane Exosome- Apoptotic Feature Exosomes Microvesicles Ectosomes particles like vesicles vesicles Size 50-100 nm 100-1,000 nm 50-200 nm 50-80 nm 20-50 nm 50-500 nm Density in 1.13-1.19 g/ml 1.04-1.07 g/ml 1.1 g/ml 1.16-1.28 sucrose g/ml EM Cup shape Irregular Bilamellar Round Irregular Hetero- appearance shape, round shape geneous electron structures dense Sedimentation 100,000 g 10,000 g 160,000- 100,000- 175,000 g 1,200 g, 200,000 g 200,000 g 10,000 g, 100,000 g Lipid Enriched in Expose PPS Enriched in No lipid composition cholesterol, cholesterol and rafts sphingomyelin diacylglycerol; and ceramide; expose PPS contains lipid rafts; expose PPS Major protein Tetraspanins Integrins, CR1 and CD133; no TNFRI Histones markers (e.g., CD63, selectins and proteolytic CD63 CD9), Alix, CD40 ligand enzymes; no TSG101 CD63 Intracellular Internal Plasma Plasma Plasma origin compartments membrane membrane membrane (endosomes) Abbreviations: phosphatidylserine (PPS); electron microscopy (EM)

Vesicles include shed membrane bound particles, or “microparticles,” that are derived from either the plasma membrane or an internal membrane. Vesicles can be released into the extracellular environment from cells. Cells releasing vesicles include without limitation cells that originate from, or are derived from, the ectoderm, endoderm, or mesoderm. The cells may have undergone genetic, environmental, and/or any other variations or alterations. For example, the cell can be tumor cells. A vesicle can reflect any changes in the source cell, and thereby reflect changes in the originating cells, e.g., cells having various genetic mutations. In one mechanism, a vesicle is generated intracellularly when a segment of the cell membrane spontaneously invaginates and is ultimately exocytosed (see for example, Keller et al., Immunol. Lett. 107 (2): 102-8 (2006)). Vesicles also include cell-derived structures bounded by a lipid bilayer membrane arising from both herniated evagination (blebbing) separation and sealing of portions of the plasma membrane or from the export of any intracellular membrane-bounded vesicular structure containing various membrane-associated proteins of tumor origin, including surface-bound molecules derived from the host circulation that bind selectively to the tumor-derived proteins together with molecules contained in the vesicle lumen, including but not limited to tumor-derived microRNAs or intracellular proteins. Blebs and blebbing are further described in Charras et al., Nature Reviews Molecular and Cell Biology, Vol. 9, No. 11, p. 730-736 (2008). A vesicle shed into circulation or bodily fluids from tumor cells may be referred to as a “circulating tumor-derived vesicle.” When such vesicle is an exosome, it may be referred to as a circulating-tumor derived exosome (CTE). In some instances, a vesicle can be derived from a specific cell of origin. CTE, as with a cell-of-origin specific vesicle, typically have one or more unique biomarkers that permit isolation of the CTE or cell-of-origin specific vesicle, e.g., from a bodily fluid and sometimes in a specific manner. For example, a cell or tissue specific markers are utilized to identify the cell of origin. Examples of such cell or tissue specific markers are disclosed herein and can further be accessed in the Tissue-specific Gene Expression and Regulation (TiGER) Database, available at bioinfo.wilmer.jhu.edu/tiger/; Liu et al. (2008) TiGER: a database for tissue-specific gene expression and regulation. BMC Bioinformatics. 9:271; TissueDistributionDBs, available at genome dkfz-heidelberg.de/menu/tissue_db/index.html.

A vesicle can have a diameter of greater than about 10 nm, 20 nm, or 30 nm. A vesicle can have a diameter of greater than 40 nm, 50 nm, 100 nm, 200 nm, 500 nm, 1000 nm or greater than 10,000 nm. A vesicle can have a diameter of about 30-1000 nm, about 30-800 nm, about 30-200 nm, or about 30-100 nm. In some embodiments, the vesicle has a diameter of less than 10,000 nm, 1000 nm, 800 nm, 500 nm, 200 nm, 100 nm, 50 nm, 40 nm, 30 nm, 20 nm or less than 10 nm. As used herein the term “about” in reference to a numerical value means that variations of 10% above or below the numerical value are within the range ascribed to the specified value. Typical sizes for various types of vesicles are shown in Table 2. Vesicles can be assessed to measure the diameter of a single vesicle or any number of vesicles. For example, the range of diameters of a vesicle population or an average diameter of a vesicle population can be determined. Vesicle diameter can be assessed using methods known in the art, e.g., imaging technologies such as electron microscopy. In an embodiment, a diameter of one or more vesicles is determined using optical particle detection. See, e.g., U.S. Pat. No. 7,751,053, entitled “Optical Detection and Analysis of Particles” and issued Jul. 6, 2010; and U.S. Pat. No. 7,399,600, entitled “Optical Detection and Analysis of Particles” and issued Jul. 15, 2010.

In some embodiments, vesicles are directly assayed from a biological sample without prior isolation, purification, or concentration from the biological sample. For example, the amount of vesicles in the sample can by itself provide a biosignature that provides a diagnostic, prognostic or theranostic determination. Alternatively, the vesicle in the sample may be isolated, captured, purified, or concentrated from a sample prior to analysis. As noted, isolation, capture or purification as used herein comprises partial isolation, partial capture or partial purification apart from other components in the sample. Vesicle isolation can be performed using various techniques as described herein, e.g., chromatography, filtration, centrifugation, flow cytometry, affinity capture (e.g., to a planar surface or bead), and/or using microfluidics.

Vesicles such as exosomes can be assessed to provide a phenotypic characterization by comparing vesicle characteristics to a reference. In some embodiments, surface antigens on a vesicle are assessed. A vesicle or vesicle population carrying a specific marker can be referred to as a positive (biomarker+) vesicle or vesicle population. For example, a DLL4+ population refers to a vesicle population associated with DLL4. Conversely, a DLL4-population would not be associated with DLL4. The surface antigens can provide an indication of the anatomical origin and/or cellular of the vesicles and other phenotypic information, e.g., tumor status. For example, wherein vesicles found in a patient sample, e.g., a bodily fluid such as blood, serum or plasma, are assessed for surface antigens indicative of colorectal origin and the presence of cancer. The surface antigens may comprise any informative biological entity that can be detected on the vesicle membrane surface, including without limitation surface proteins, lipids, carbohydrates, and other membrane components. For example, positive detection of colon derived vesicles expressing tumor antigens can indicate that the patient has colorectal cancer. As such, methods of the invention can be used to characterize any disease or condition associated with an anatomical or cellular origin, by assessing, for example, disease-specific and cell-specific biomarkers of one or more vesicles obtained from a subject.

In another embodiment, one or more vesicle payloads are assessed to provide a phenotypic characterization. The payload with a vesicle comprises any informative biological entity that can be detected as encapsulated within the vesicle, including without limitation proteins and nucleic acids, e.g., genomic or cDNA, mRNA, or functional fragments thereof, as well as microRNAs (miRs). In addition, methods of the invention are directed to detecting vesicle surface antigens (in addition or exclusive to vesicle payload) to provide a phenotypic characterization. For example, vesicles can be characterized by using binding agents (e.g., antibodies or aptamers) that are specific to vesicle surface antigens, and the bound vesicles can be further assessed to identify one or more payload components disclosed therein. As described herein, the levels of vesicles with surface antigens of interest or with payload of interest can be compared to a reference to characterize a phenotype. For example, overexpression in a sample of cancer-related surface antigens or vesicle payload, e.g., a tumor associated mRNA or microRNA, as compared to a reference, can indicate the presence of cancer in the sample. The biomarkers assessed can be present or absent, increased or reduced based on the selection of the desired target sample and comparison of the target sample to the desired reference sample. Non-limiting examples of target samples include: disease; treated/not-treated; different time points, such as a in a longitudinal study; and non-limiting examples of reference sample: non-disease; normal; different time points; and sensitive or resistant to candidate treatment(s).

MicroRNA

Various biomarker molecules can be assessed in biological samples or vesicles obtained from such biological samples. MicroRNAs comprise one class biomarkers assessed via methods of the invention. MicroRNAs, also referred to herein as miRNAs or miRs, are short RNA strands approximately 21-23 nucleotides in length. MiRNAs are encoded by genes that are transcribed from DNA but are not translated into protein and thus comprise non-coding RNA. The miRs are processed from primary transcripts known as pri-miRNA to short stem-loop structures called pre-miRNA and finally to the resulting single strand miRNA. The pre-miRNA typically forms a structure that folds back on itself in self-complementary regions. These structures are then processed by the nuclease Dicer in animals or DCL1 in plants. Mature miRNA molecules are partially complementary to one or more messenger RNA (mRNA) molecules and can function to regulate translation of proteins. Identified sequences of miRNA can be accessed at publicly available databases, such as www.microRNA.org, www.mirbase.org, or www.mirz.unibas.ch/cgi/miRNA.cgi.

miRNAs are generally assigned a number according to the naming convention “mir-[number].” The number of a miRNA is assigned according to its order of discovery relative to previously identified miRNA species. For example, if the last published miRNA was mir-121, the next discovered miRNA will be named mir-122, etc. When a miRNA is discovered that is homologous to a known miRNA from a different organism, the name can be given an optional organism identifier, of the form [organism identifier]-mir-[number]. Identifiers include hsa for Homo sapiens and mmu for Mus Musculus. For example, a human homolog to mir-121 might be referred to as hsa-mir-121 whereas the mouse homolog can be referred to as mmu-mir-121.

Mature microRNA is commonly designated with the prefix “miR” whereas the gene or precursor miRNA is designated with the prefix “mir.” For example, mir-121 is a precursor for miR-121. When differing miRNA genes or precursors are processed into identical mature miRNAs, the genes/precursors can be delineated by a numbered suffix. For example, mir-121-1 and mir-121-2 can refer to distinct genes or precursors that are processed into miR-121. Lettered suffixes are used to indicate closely related mature sequences. For example, mir-121a and mir-121b can be processed to closely related miRNAs miR-121a and miR-121b, respectively. In the context of the invention, any microRNA (miRNA or miR) designated herein with the prefix mir-* or miR-* is understood to encompass both the precursor and/or mature species, unless otherwise explicitly stated otherwise.

Sometimes it is observed that two mature miRNA sequences originate from the same precursor. When one of the sequences is more abundant that the other, a “*” suffix can be used to designate the less common variant. For example, miR-121 would be the predominant product whereas miR-121* is the less common variant found on the opposite arm of the precursor. If the predominant variant is not identified, the miRs can be distinguished by the suffix “5p” for the variant from the 5′ arm of the precursor and the suffix “3p” for the variant from the 3′ arm. For example, miR-121-5p originates from the 5′ arm of the precursor whereas miR-121-3p originates from the 3′ arm. Less commonly, the 5p and 3p variants are referred to as the sense (“s”) and anti-sense (“as”) forms, respectively. For example, miR-121-5p may be referred to as miR-121-s whereas miR-121-3p may be referred to as miR-121-as.

The above naming conventions have evolved over time and are general guidelines rather than absolute rules. For example, the let- and lin-families of miRNAs continue to be referred to by these monikers. The mir/miR convention for precursor/mature forms is also a guideline and context should be taken into account to determine which form is referred to. Further details of miR naming can be found at www.mirbase.org or Ambros et al., A uniform system for microRNA annotation, RNA 9:277-279 (2003).

Plant miRNAs follow a different naming convention as described in Meyers et al., Plant Cell. 2008 20(12):3186-3190.

A number of miRNAs are involved in gene regulation, and miRNAs are part of a growing class of non-coding RNAs that is now recognized as a major tier of gene control. In some cases, miRNAs can interrupt translation by binding to regulatory sites embedded in the 3′-UTRs of their target mRNAs, leading to the repression of translation. Target recognition involves complementary base pairing of the target site with the miRNA's seed region (positions 2-8 at the miRNA's 5′ end), although the exact extent of seed complementarity is not precisely determined and can be modified by 3′ pairing. In other cases, miRNAs function like small interfering RNAs (siRNA) and bind to perfectly complementary mRNA sequences to destroy the target transcript.

Characterization of a number of miRNAs indicates that they influence a variety of processes, including early development, cell proliferation and cell death, apoptosis and fat metabolism. For example, some miRNAs, such as lin-4, let-7, mir-14, mir-23, and bantam, have been shown to play critical roles in cell differentiation and tissue development. Others are believed to have similarly important roles because of their differential spatial and temporal expression patterns.

The miRNA database available at miRBase (www.mirbase.org) comprises a searchable database of published miRNA sequences and annotation. Further information about miRBase can be found in the following articles, each of which is incorporated by reference in its entirety herein: Griffiths-Jones et al., miRBase: tools for microRNA genomics. NAR 2008 36(Database Issue):D154-D158; Griffiths-Jones et al., miRBase: microRNA sequences, targets and gene nomenclature. NAR 2006 34(Database Issue):D140-D144; and Griffiths-Jones, S. The microRNA Registry. NAR 2004 32(Database Issue):D109-D111. Representative miRNAs contained in Release 16 of miRBase, made available September 2010.

As described herein, microRNAs are known to be involved in cancer and other diseases and can be assessed in order to characterize a phenotype in a sample. See, e.g., Ferracin et al., Micromarkers: miRNAs in cancer diagnosis and prognosis, Exp Rev Mol Diag, April 2010, Vol. 10, No. 3, Pages 297-308; Fabbri, miRNAs as molecular biomarkers of cancer, Exp Rev Mol Diag, May 2010, Vol. 10, No. 4, Pages 435-444. Techniques to isolate and characterize vesicles and miRs are known to those of skill in the art. In addition to the methodology presented herein, additional methods can be found in U.S. Pat. No. 7,888,035, entitled “METHODS FOR ASSESSING RNA PATTERNS” and issued Feb. 15, 2011; and International Patent Application Nos. PCT/US2010/058461, entitled “METHODS AND SYSTEMS FOR ISOLATING, STORING, AND ANALYZING VESICLES” and filed Nov. 30, 2010; and PCT/US2011/021160, entitled “DETECTION OF GASTROINTESTINAL DISORDERS” and filed Jan. 13, 2011; each of which applications are incorporated by reference herein in their entirety.

Circulating Biomarkers

Circulating biomarkers include biomarkers that are detectable in body fluids, such as blood, plasma, serum. Examples of circulating cancer biomarkers include cardiac troponin T (cTnT), prostate specific antigen (PSA) for prostate cancer and CA125 for ovarian cancer. Circulating biomarkers according to the invention include any appropriate biomarker that can be detected in bodily fluid, including without limitation protein, nucleic acids, e.g., DNA, mRNA and microRNA, lipids, carbohydrates and metabolites. Circulating biomarkers can include biomarkers that are not associated with cells, such as biomarkers that are membrane associated, embedded in membrane fragments, part of a biological complex, or free in solution. In one embodiment, circulating biomarkers are biomarkers that are associated with one or more vesicles present in the biological fluid of a subject.

Circulating biomarkers have been identified for use in characterization of various phenotypes. See, e.g., Ahmed N, et al., Proteomic-based identification of haptoglobin-1 precursor as a novel circulating biomarker of ovarian cancer. Br. J. Cancer 2004; Mathelin et al., Circulating proteinic biomarkers and breast cancer, Gynecol Obstet Fertil. 2006 July-August; 34(7-8):638-46. Epub 2006 July 28; Ye et al., Recent technical strategies to identify diagnostic biomarkers for ovarian cancer. Expert Rev Proteomics. 2007 February; 4(1):121-31; Carney, Circulating oncoproteins HER2/neu, EGFR and CAIX (MN) as novel cancer biomarkers. Expert Rev Mol Diagn. 2007 May; 7(3):309-19; Gagnon, Discovery and application of protein biomarkers for ovarian cancer, Curr Opin Obstet. Gynecol. 2008 February; 20(1):9-13; Pasterkamp et al., Immune regulatory cells: circulating biomarker factories in cardiovascular disease. Clin Sci (Load). 2008 August; 115(4):129-31; Fabbri, miRNAs as molecular biomarkers of cancer, Exp Rev Mol Diag, May 2010, Vol. 10, No. 4, Pages 435-444; PCT Patent Publication WO/2007/088537; U.S. Pat. Nos. 7,745,150 and 7,655,479; U.S. Patent Publications 20110008808, 20100330683, 20100248290, 20100222230, 20100203566, 20100173788, 20090291932, 20090239246, 20090226937, 20090111121, 20090004687, 20080261258, 20080213907, 20060003465, 20050124071, and 20040096915, each of which publication is incorporated herein by reference in its entirety.

Vesicle Enrichment

A vesicle or a population of vesicles may be isolated, purified, concentrated or otherwise enriched prior to and/or during analysis. Unless otherwise specified, the terms “purified,” “isolated,” “ ” as used herein in reference to vesicles or biomarker components include partial or complete purification or isolation of such components from a cell or organism. Analysis of a vesicle can include quantitiating the amount one or more vesicle populations of a biological sample. For example, a heterogeneous population of vesicles can be quantitated, or a homogeneous population of vesicles, such as a population of vesicles with a particular biomarker profile, a particular biosignature, or derived from a particular cell type can be isolated from a heterogeneous population of vesicles and quantitated. Analysis of a vesicle can also include detecting, quantitatively or qualitatively, one or more particular biomarker profile or biosignature of a vesicle, as described herein.

A vesicle can be stored and archived, such as in a bio-fluid bank and retrieved for analysis as necessary. A vesicle may also be isolated from a biological sample that has been previously harvested and stored from a living or deceased subject. In addition, a vesicle may be isolated from a biological sample which has been collected as described in King et al., Breast Cancer Res 7(5): 198-204 (2005). A vesicle can be isolated from an archived or stored sample. Alternatively, a vesicle may be isolated from a biological sample and analyzed without storing or archiving of the sample. Furthermore, a third party may obtain or store the biological sample, or obtain or store the vesicle for analysis.

An enriched population of vesicles can be obtained from a biological sample. For example, vesicles may be concentrated or isolated from a biological sample using size exclusion chromatography, density gradient centrifugation, differential centrifugation, nanomembrane ultrafiltration, immunoabsorbent capture, affinity purification, microfluidic separation, or combinations thereof.

Size exclusion chromatography, such as gel permeation columns, centrifugation or density gradient centrifugation, and filtration methods can be used. For example, a vesicle can be isolated by differential centrifugation, anion exchange and/or gel permeation chromatography (for example, as described in U.S. Pat. Nos. 6,899,863 and 6,812,023), sucrose density gradients, organelle electrophoresis (for example, as described in U.S. Pat. No. 7,198,923), magnetic activated cell sorting (MACS), or with a nanomembrane ultrafiltration concentrator. Various combinations of isolation or concentration methods can be used.

Highly abundant proteins, such as albumin and immunoglobulin, may hinder isolation of vesicles from a biological sample. For example, a vesicle can be isolated from a biological sample using a system that utilizes multiple antibodies that are specific to the most abundant proteins found in a biological sample, such as blood. Such a system can remove up to several proteins at once, thus unveiling the lower abundance species such as cell-of-origin specific vesicles.

This type of system can be used for isolation of vesicles from biological samples such as blood, cerebrospinal fluid or urine. The isolation of vesicles from a biological sample may also be enhanced by high abundant protein removal methods as described in Chromy et al. J Proteome Res 2004; 3:1120-1127. In another embodiment, the isolation of vesicles from a biological sample may also be enhanced by removing serum proteins using glycopeptide capture as described in Zhang et al, Mol Cell Proteomics 2005; 4:144-155. In addition, vesicles from a biological sample such as urine may be isolated by differential centrifugation followed by contact with antibodies directed to cytoplasmic or anti-cytoplasmic epitopes as described in Pisitkun et al., Proc Natl Acad Sci USA, 2004; 101:13368-13373.

Isolation or enrichment of a vesicle from a biological sample can also be enhanced by use of sonication (for example, by applying ultrasound), detergents, other membrane-activating agents, or any combination thereof. For example, ultrasonic energy can be applied to a potential tumor site, and without being bound by theory, release of vesicles from a tissue can be increased, allowing an enriched population of vesicles that can be analyzed or assessed from a biological sample using one or more methods disclosed herein.

Sample Handling

With methods of detecting circulating biomarkers as described here, e.g., antibody affinity isolation, the consistency of the results can be optimized as necessary using various concentration or isolation procedures. Such steps can include agitation such as shaking or vortexing, different isolation techniques such as polymer based isolation, e.g., with PEG, and concentration to different levels during filtration or other steps. It will be understood by those in the art that such treatments can be applied at various stages of testing the vesicle containing sample. In one embodiment, the sample itself, e.g., a bodily fluid such as plasma or serum, is vortexed. In some embodiments, the sample is vortexed after one or more sample treatment step, e.g., vesicle isolation, has occurred. Agitation can occur at some or all appropriate sample treatment steps as desired. Additives can be introduced at the various steps to improve the process, e.g., to control aggregation or degradation of the biomarkers of interest.

The results can also be optimized as desirable by treating the sample with various agents. Such agents include additives to control aggregation and/or additives to adjust pH or ionic strength. Additives that control aggregation include blocking agents such as bovine serum albumin (BSA) and milk, chaotropic agents such as guanidium hydro chloride, and detergents or surfactants. Useful ionic detergents include sodium dodecyl sulfate (SDS, sodium lauryl sulfate (SLS)), sodium laureth sulfate (SLS, sodium lauryl ether sulfate (SLES)), ammonium lauryl sulfate (ALS), cetrimonium bromide, cetrimonium chloride, cetrimonium stearate, and the like. Useful non-ionic (zwitterionic) detergents include polyoxyethylene glycols, polysorbate 20 (also known as Tween 20), other polysorbates (e.g., 40, 60, 65, 80, etc), Triton-X (e.g., X100, X114), 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS), CHAPSO, deoxycholic acid, sodium deoxycholate, NP-40, glycosides, octyl-thio-glucosides, maltosides, and the like. In some embodiments, Pluronic F-68, a surfactant shown to reduce platelet aggregation, is used to treat samples containing vesicles during isolation and/or detection. F68 can be used from a 0.1% to 10% concentration, e.g., a 1%, 2.5% or 5% concentration. The pH and/or ionic strength of the solution can be adjusted with various acids, bases, buffers or salts, including without limitation sodium chloride (NaCl), phosphate-buffered saline (PBS), tris-buffered saline (TBS), sodium phosphate, potassium chloride, potassium phosphate, sodium citrate and saline-sodium citrate (SSC) buffer. In some embodiments, NaCl is added at a concentration of 0.1% to 10%, e.g., 1%, 2.5% or 5% final concentration. In some embodiments, Tween 20 is added to 0.005 to 2% concentration, e.g., 0.05%, 0.25% or 0.5% final concentration. Blocking agents for use with the invention comprise inert proteins, e.g., milk proteins, non-fat dry milk protein, albumin, BSA, casein, or serum such as newborn calf serum (NBCS), goat serum, rabbit serum or salmon serum. The proteins can be added at a 0.1% to 10% concentration, e.g., 1%, 2%, 3%, 3.5%, 4%, 5%, 6%, 7%, 8%, 9% or 10% concentration. In some embodiments, BSA is added to 0.1% to 10% concentration, e.g., 1%, 2%, 3%, 3.5%, 4%, 5%, 6%, 7%, 8%, 9% or 10% concentration. In an embodiment, the sample is treated according to the methodology presented in U.S. patent application Ser. No. 11/632,946, filed Jul. 13, 2005, which application is incorporated herein by reference in its entirety. Commercially available blockers may be used, such as SuperBlock, StartingBlock, Protein-Free from Pierce (a division of Thermo Fisher Scientific, Rockford, Ill.). In some embodiments, SSC/detergent (e.g., 20×SSC with 0.5% Tween 20 or 0.1% Triton-X 100) is added to 0.1% to 10% concentration, e.g., at 1.0% or 5.0% concentration.

The methods of detecting vesicles and other circulating biomarkers can be optimized as desired with various combinations of protocols and treatments as described herein. A detection protocol can be optimized by various combinations of agitation, isolation methods, and additives. In some embodiments, the patient sample is vortexed before and after isolation steps, and the sample is treated with blocking agents including BSA and/or F68. Such treatments may reduce the formation of large aggregates or protein or other biological debris and thus provide a more consistent detection reading.

Filters

A vesicle can be isolated from a biological sample by filtering the sample through a filtration module comprising a filter and collecting a retentate comprising the vesicle, thereby isolating the vesicle from the biological sample. The filtration module can be adjusted to facilitate the isolation of the desired molecules. In some embodiments, the filter retains molecules greater than 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, or 500 kiloDaltons.

The isolation can also comprise applying the retentate to one or more substrates, wherein each substrate is coupled to one or more capture agents. In embodiments, each subset of the plurality of substrates comprises a different capture agent or combination of capture agents than another subset of the plurality of substrates. In this manner, different subpopulations of vesicles can be isolated. In some embodiments, a biosignature of the vesicle is determined.

In an aspect, the invention provides a method of determining a biosignature of a vesicle in a sample comprising: filtering a biological sample from a subject with a disorder through a filtration module, collecting from the filtration module a retentate comprising one or more vesicles, and determining a biosignature of the one or more vesicles. In some embodiments, the filter retains molecules greater than 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, or 500 kiloDaltons. In one embodiment, the filtration module comprises a filter that retains molecules greater than about 100 or 150 kiloDaltons.

The filtration methods of the invention can further comprise characterizing a phenotype in a subject by filtering a biological sample from a subject through a filtration module, collecting from the filtration module a retentate comprising one or more vesicles; detecting a biosignature of the one or more vesicles; and characterizing a phenotype in the subject based on the biosignature, wherein characterizing is performed with a requisite level of sensitivity and specificity. In some embodiments, the method provides at least 50%, 60%, 70%, 80%, 90% or 95% sensitivity and at least 50%, 60%, 70%, 80%, 90% or 95% specificity. In some embodiments, characterizing comprises determining an amount of one or more vesicles having the biosignature.

In an aspect, the invention provides a method for multiplex analysis of a plurality of vesicles. The method comprises filtering a biological sample from a subject through a filtration module; collecting from the filtration module a retentate comprising the plurality of vesicles, applying the plurality of vesicles to a plurality of capture agents, wherein the plurality of capture agents is coupled to a plurality of substrates, and wherein each subset of the plurality of substrates is optionally differentially labeled from another subset of the plurality of substrates; capturing at least a subset of the plurality of vesicles with the capture agents; and determining a biosignature for at least a subset of the captured vesicles. In one embodiment, each substrate is coupled to one or more capture agents, and each subset of the plurality of substrates comprises a different capture agent or combination of capture agents as compared to another subset of the plurality of substrates. In some embodiments, at least a subset of the plurality of substrates is intrinsically labeled, such as comprising one or more labels. The substrate can be a particle or bead, or any combination thereof. In some embodiments, the filter retains molecules greater than 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, or 500 kiloDaltons. In one embodiment, the filtration module comprises a filter that retains molecules greater than about 100 or 150 kiloDaltons. In one embodiment, the filtration module comprises a filter that retains molecules greater than about 9, 20 or 150 kiloDaltons.

A related method for multiplex analysis of a plurality of vesicles comprises filtering a biological sample from a subject through a filtration module, wherein the filtration module comprises a filter that retains molecules greater than about 10 kiloDaltons; collecting from the filtration module a retentate comprising the plurality of vesicles; applying the plurality of vesicles to a plurality of capture agents, wherein the plurality of capture agents is coupled to a microarray; capturing at least a subset of the plurality of vesicles on the microarray; and determining a biosignature for at least a subset of the captured vesicles. In some embodiments, the filter retains molecules greater than 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, or 500 kiloDaltons. In one embodiment, the filtration module comprises a filter that retains molecules greater than 100 or 150 kiloDaltons. In one embodiment, the filtration module comprises a filter that retains molecules greater than about 9, 20 or 150 kiloDaltons.

In the methods of the invention, the biological sample to be filtered can be clarified prior to isolation by filtration. Clarification comprises selective removal of cellular debris and other undesirable materials, e.g., non-vesicle components. In some embodiments, clarification comprises low-speed centrifugation, such as centrifugation at about 5,000×g, 4,000×g, 3,000×g, 2,000×g, 1,000×g. In some embodiments, clarification of less than 1,000×g is used. The supernatant, or clarified biological sample, containing the vesicle can then be collected and filtered to isolate the vesicle from the clarified biological sample. In some embodiments, the biological sample is not clarified prior to isolation of a vesicle by filtration.

In some embodiments, isolation of a vesicle from a sample does not use high-speed centrifugation, such as ultracentrifugation. Isolation can avoid the use of high-speed centrifugal speeds, such as about 100,000×g or more. In some embodiments, isolation of a vesicle from a sample uses speeds of less than 50,000×g, 40,000×g, 30,000×g, 20,000×g, 15,000×g, 12,000×g, or less than 10,000×g.

Without being bound by theory, microvesicles may be compressed due to high-speed centrifugation, such as ultracentrifugation, which may remove protein targets weakly anchored in the microvesicle membrane as opposed to the tetraspanins which are more solidly anchored in the membrane, resulting in reduced cell specific targets in the microvesicle membrane, and thus inability to detect particular biomarkers during analysis of the microvesicle.

Any number of applicable filter configurations can be used to filter vesicle-containing samples. In some embodiments, the filtration module used to isolate the vesicle from the biological sample is a fiber-based filtration cartridge. Fibers include hollow polymeric fibers, such as a polypropylene hollow fiber. A biological sample can be introduced into the filtration module by pumping the sample fluid, such as a biological fluid as disclosed herein, into the module with a pump device, such as a peristaltic pump. The pump flow rate can vary, such as at about 0.25, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, or 10 mL/minute. The flow rate can be adjusted given the configuration, e.g., size and throughput, of the filtration module.

In some embodiments the filtration module used to isolate the vesicle from the biological sample is a membrane filtration module. The membrane filtration module can comprise a filter disc membrane, such as a hydrophilic polyvinylidene difluoride (PVDF) filter disc membrane housed in a stirred cell apparatus (e.g., comprising a magnetic stirrer). In some embodiments, the sample moves through the filter as a result of a pressure gradient established on either side of the filter membrane.

The filter can comprise a material having low hydrophobic absorptivity and/or high hydrophilic properties. The filter can have an average pore size selected for vesicle retention and permeation of most proteins as well as a surface that is hydrophilic, thereby limiting protein adsorption. In some embodiments, the filter comprises a material selected from the group consisting of polypropylene, PVDF, polyethylene, polyfluoroethylene, cellulose, secondary cellulose acetate, polyvinylalcohol, and ethylenevinyl alcohol (EVAL®, Kuraray Co., Okayama, Japan). Additional materials that can be used in a filter include, but are not limited to, polysulfone and polyethersulfone.

The filtration module can have a filter that retains molecules greater than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 400, or 500 kiloDaltons (kDa), such as a filter that has a MWCO (molecular weight cut off) of about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 400, or 500 kDa. Ultrafiltration membranes with a range of MWCO of 9 kDa, 20 kDa and/or 150 kDa can be used. In some embodiments, the filter within the filtration module has an average pore diameter of about 0.01 μm to about 0.15 μm, and in some embodiments from about 0.05 μm to about 0.12 μm. In some embodiments, the filter has an average pore diameter of about 0.06 μm, 0.07 μm, 0.08 μm, 0.09 μm, 0.1 μm, 0.11 μm, or 0.2 μm.

Commercially available filtration module can be used in the methods of the invention, such as a column typically used for concentrating proteins or for isolating proteins. Examples include, but are not limited to, columns from Millipore (Billerica, Mass.), such as Amicon® centrifugal filters, or from Pierce® (Rockford, Ill.), such as Pierce Concentrator filter devices. Useful columns from Pierce include disposable ultrafiltration centrifugal devices with a MWCO of 9 kDa, 20 kDa and/or 150 kDa. These concentrators consist of a high-performance regenerated cellulose membrane welded to a conical device. The filters can be as described in U.S. Pat. No. 6,269,957 or 6,357,601, both of which applications are incorporated by reference in their entirety herein.

In the methods of the invention, the retentate comprising the isolated devices for concentrating proteins vesicle is typically collected from the filtration module. The retentate can be collected by flushing the retentate from the filter. Selection of a filter composition having hydrophilic surface properties, thereby limiting protein adsorption, can be used for easier collection of the retentate, e.g., to minimize use of harsh or time-consuming collection techniques.

The collected retentate can then be used for subsequent analysis, such as assessing a biosignature of one or more vesicles in the retentate, as further described herein. The analysis can be directly performed on the collected retentate. Alternatively, the collected retentate can be further concentrated or purified prior to analysis of one or more vesicles. In some embodiments, the retentate is further concentrated or vesicles further isolated from the retentate using another filtration step, size exclusion chromatography, density gradient centrifugation, differential centrifugation, immunoabsorbent capture, affinity purification, microfluidic separation, or combinations thereof, such as described herein. Vesicle can also be concentrated or isolated prior to any filtration steps, e.g., using size exclusion chromatography, density gradient centrifugation, differential centrifugation, immunoabsorbent capture, affinity purification, microfluidic separation, or combinations thereof.

Combinations of filters can be used for concentrating and isolating vesicles. For example, the biological sample may first be filtered through a filter having a porosity or pore size of between about 0.01 μm to about 2 μm, about 0.05 μm to about 1.5 μm, and then the sample is filtered through a filtration module with a filter that retains molecules greater than about 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 400, or 500 kiloDaltons (kDa), such as a filter that has a MWCO (molecular weight cut off) of about 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 400, or 500 kDa. In some embodiments, filters are used having a pore size of about 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 or 2.0 μm. The filter may be a syringe filter. As a non-limiting example, one embodiment comprises filtering the biological sample through a filter, such as a syringe filter, wherein the syringe filter has a porosity of greater than about 1 μm, prior to filtering the sample through a filtration module comprising a filter that retains molecules greater than about 100 or 150 kiloDaltons. In an embodiment, the filter is 1.2 μM filter and the filtration is followed by passage of the sample through a 7 ml concentrator column with a 150 kDa cutoff.

The filtration module can be a component of a microfluidic device. Microfluidic devices, which are also referred to as “lab-on-a-chip” systems, biomedical micro-electro-mechanical systems (bioMEMs), or multicomponent integrated systems, can be used for isolating and analyzing vesicles. Such systems miniaturize and compartmentalize processes that allow for binding of vesicles, detection of biomarkers, and other processes, such as further described herein.

In an embodiment, a microfluidic device used for isolation of a vesicle comprises a filtration module. A biological sample can be introduced into one or more microfluidic channels, which selectively allows the passage of vesicles, e.g., by filtering or otherwise separating based on particle size. The microfluidic device can also comprise a plurality of filtration modules, binding agents, or other separation modules to select vesicles based on their properties such as size, shape, deformability, biomarker profile, or biosignature.

In one embodiment, a vesicle is isolated from a biological sample using filtration by size and mass. Filtration can be sequential, such as first filtering by size and then by mass, or alternatively, first by mass, and then by size. For example, plasma can be separated from whole blood, then physically filtrated using a syringe by size, then by column filtration to select by mass, resulting in a vesicle being isolated from plasma. FIG. 87B represents a schematic of compression of a membrane of a vesicle due to high-speed centrifugation, such as ultracentrifugation. Such high-speed centrifugation may remove protein targets weakly anchored in the membrane as opposed to the tetraspanins which are more solidly anchored in the membrane. Without being bound by theory, ultracentrifugation may in some case reduce the cell specific targets in the vesicle, and thus not be detected in subsequent analysis of the biosignature of the vesicle. Thus, advantages of such a method can include consistent yields, less lipid damage, preservation of biomarkers, and the ability to filter for both size and mass.

Binding Agents

Binding agents (also referred to as binding reagents) include agents that are capable of binding a target biomarker. A binding agent can be specific for the target biomarker, meaning the agent is capable of binding a target biomarker. The target can be any useful biomarker disclosed herein, such as a biomarker on the vesicle surface. In some embodiments, the target is a single molecule, such as a single protein, so that the binding agent is specific to the single protein. In other embodiments, the target can be a group of molecules, such as a family or proteins having a similar epitope or moiety, so that the binding agent is specific to the family or group of proteins. The group of molecules can also be a class of molecules, such as protein, DNA or RNA. The binding agent can be a capture agent used to capture a vesicle by binding a component or biomarker of a vesicle. In some embodiments, a capture agent comprises an antibody or fragment thereof, or an aptamer, that binds to an antigen on a vesicle. The capture agent can be optionally coupled to a substrate and used to isolate a vesicle, as further described herein.

A binding agent is an agent that binds to a circulating biomarker, such as a vesicle or a component of a vesicle. The binding agent can be used as a capture agent and/or a detection agent. A capture agent can bind and capture a circulating biomarker, such as by binding a component or biomarker of a vesicle. For example, the capture agent can be a capture antibody or capture antigen that binds to an antigen on a vesicle. A detection agent can bind to a circulating biomarker thereby facilitating detection of the biomarker. For example, a capture agent comprising an antibody or aptamer that is sequestered to a substrate can be used to capture a vesicle in a sample, and a detection agent comprising an antibody or aptamer that carries a label can be used to detect the captured vesicle via detection of the detection agent's label. In some embodiments, a vesicle is assessed using capture and detection agents that recognize the same vesicle biomarkers. For example, a vesicle population can be captured using a tetraspanin such as by using an anti-CD9 antibody bound to a substrate, and the captured vesicles can be detected using a fluorescently labeled anti-CD9 antibody to label the captured vesicles. In other embodiments, a vesicle is assessed using capture and detection agents that recognize different vesicle biomarkers. For example, a vesicle population can be captured using a cell-specific marker such as by using an anti-PCSA antibody bound to a substrate, and the captured vesicles can be detected using a fluorescently labeled anti-CD9 antibody to label the captured vesicles. Similarly, the vesicle population can be captured using a general vesicle marker such as by using an anti-CD9 antibody bound to a substate, and the captured vesicles can be detected using a fluorescently labeled antibody to a cell-specific or disease specific marker to label the captured vesicles.

The biomarkers recognized by the binding agent are sometimes referred to herein as an antigen. Unless otherwise specified, antigen as used herein is meant to encompass any entity that is capable of being bound by a binding agent, regardless of the type of binding agent or the immunogenicity of the biomarker. The antigen further encompasses a functional fragment thereof. For example, an antigen can encompass a protein biomarker capable of being bound by a binding agent, including a fragment of the protein that is capable of being bound by a binding agent.

In one embodiment, a vesicle is captured using a capture agent that binds to a biomarker on a vesicle. The capture agent can be coupled to a substrate and used to isolate a vesicle, as further described herein. In one embodiment, a capture agent is used for affinity capture or isolation of a vesicle present in a substance or sample.

A binding agent can be used after a vesicle is concentrated or isolated from a biological sample. For example, a vesicle can first be isolated from a biological sample before a vesicle with a specific biosignature is isolated or detected. The vesicle with a specific biosignature can be isolated or detected using a binding agent for the biomarker. A vesicle with the specific biomarker can be isolated or detected from a heterogeneous population of vesicles. Alternatively, a binding agent may be used on a biological sample comprising vesicles without a prior isolation or concentration step. For example, a binding agent is used to isolate or detect a vesicle with a specific biosignature directly from a biological sample.

A binding agent can be a nucleic acid, protein, or other molecule that can bind to a component of a vesicle. The binding agent can comprise DNA, RNA, monoclonal antibodies, polyclonal antibodies, Fabs, Fab′, single chain antibodies, synthetic antibodies, aptamers (DNA/RNA), peptoids, zDNA, peptide nucleic acids (PNAs), locked nucleic acids (LNAs), lectins, synthetic or naturally occurring chemical compounds (including but not limited to drugs, labeling reagents), dendrimers, or a combination thereof. For example, the binding agent can be a capture antibody, antibody fragment, or aptamer. In embodiments of the invention, the binding agent comprises a membrane protein labeling agent. See, e.g., the membrane protein labeling agents disclosed in Alroy et al., US. Patent Publication US 2005/0158708. In an embodiment, vesicles are isolated or captured as described herein, and one or more membrane protein labeling agent is used to detect the vesicles.

In some instances, a single binding agent can be employed to isolate or detect a vesicle. In other instances, a combination of different binding agents may be employed to isolate or detect a vesicle. For example, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75 or 100 different binding agents may be used to isolate or detect a vesicle from a biological sample. Furthermore, the one or more different binding agents for a vesicle can form a biosignature of a vesicle, as further described below.

Different binding agents can also be used for multiplexing. For example, isolation or detection of more than one population of vesicles can be performed by isolating or detecting each vesicle population with a different binding agent. Different binding agents can be bound to different particles, wherein the different particles are labeled which may allow the particles to be distinguished. In another embodiment, an array comprising different binding agents can be used for multiplex analysis, wherein the different binding agents are differentially labeled or can be ascertained based on the location of the binding agent on the array. Multiplexing can be accomplished up to the resolution capability of the labels or detection method, such as described below. The binding agents can be used to detect the vesicles, such as for detecting cell-of-origin specific vesicles. A binding agent or multiple binding agents can themselves form a binding agent profile that provides a biosignature for a vesicle. One or more binding agents can be selected from FIG. 2. For example, if a vesicle population is detected or isolated using two, three, four or more binding agents in a differential detection or isolation of a vesicle from a heterogeneous population of vesicles, the particular binding agent profile for the vesicle population provides a biosignature for the particular vesicle population. The vesicle can be detected using any number of binding agents in a multiplex fashion. Thus, the binding agent can also be used to form a biosignature for a vesicle. The biosignature can be used to characterize a phenotype.

The binding agent can be a lectin. Lectins are proteins that bind selectively to polysaccharides and glycoproteins and are widely distributed in plants and animals. For example, lectins such as those derived from Galanthus nivalis in the form of Galanthus nivalis agglutinin (“GNA”), Narcissus pseudonarcissus in the form of Narcissus pseudonarcissus agglutinin (“NPA”) and the blue green algae Nostoc ellipsosporum called “cyanovirin” (Boyd et al. Antimicrob Agents Chemother 41(7): 15211530, 1997; Hammar et al. Ann N Y Acad Sci 724: 166 169, 1994; Kaku et al. Arch Biochem Biophys 279(2): 298 304, 1990) can be used to isolate a vesicle. These lectins can bind to glycoproteins having a high mannose content (Chervenak et al. Biochemistry 34(16): 5685 5695, 1995). High mannose glycoprotein refers to glycoproteins having mannose-mannose linkages in the form of α-1→3 or α-1→6 mannose-mannose linkages.

The binding agent can be an agent that binds one or more lectins. Lectin capture can be applied to the isolation of the biomarker cathepsin D since it is a glycosylated protein capable of binding the lectins Galanthus nivalis agglutinin (GNA) and concanavalin A (ConA).

Methods and devices for using lectins to capture vesicles are described in International Patent Applications PCT/US2010/058461, entitled “METHODS AND SYSTEMS FOR ISOLATING, STORING, AND ANALYZING VESICLES” and filed Nov. 30, 2010; PCT/US2009/066626, entitled “AFFINITY CAPTURE OF CIRCULATING BIOMARKERS” and filed Dec. 3, 2009; PCT/US2010/037467, entitled “METHODS AND MATERIALS FOR ISOLATING EXOSOMES” and filed Jun. 4, 2010; and PCT/US2007/006101, entitled “EXTRACORPOREAL REMOVAL OF MICROVESICULAR PARTICLES” and filed Mar. 9, 2007, each of which applications is incorporated by reference herein in its entirety.

Binding agents comprise capture agents, such as an antibody or fragment thereof, or an aptamer. A vesicle can be isolated using one or more capture agents that are specific for a biomarker on a vesicle. In one embodiment, one or more antibodies specific for one or more antigens present on a vesicle are used as a capture agent for a vesicle. For example, a vesicle having CD63 on its surface can be captured with an antibody for CD63. Alternatively, a vesicle derived from a tumor cell can express EpCam, and the vesicle can be isolated or detected using a capture agent for EpCam, for CD63, or both. In various embodiments, the capture agent is an agent specific for a biomarker including CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR, or a combination thereof. The capture agent for these markers can be an antibody or antibody fragment that recognizes the markers. In some embodiments, antibodies for binding or capturing vesicles used by the methods of the invention include antibodies and fragments to CD9, PSCA, TNFR, CD63, B7H3, MFG-E8, EpCam, Rab, CD81, STEAP, PCSA, PSMA, and/or 5T4. In other embodiments, the capture agent is an antibody to CD9, CD63, CD81, PSMA, PCSA, B7H3, EpCam, PSCA, ICAM, STEAP, and/or EGFR. In another embodiment, the capture agent recognizes TMEM211 and/or CD24, such as an antibody that binds TMEM211 and/or CD24.

In some embodiments, the capture agents are used in combination to capture vesicles having more than one biomarker.

The capture agent can be used to identify a biomarker of a vesicle. For example, a capture agent such as an antibody to CD9 can be used to identify CD9 as a biomarker of the vesicle. In some embodiments, a plurality of capture agents are used together, such as in multiplex analysis. The plurality of captures agents can comprise binding agents to one or more of: CD9, CD63, CD81, PSMA, PCSA, B7H3, EpCam, PSCA, ICAM, STEAP, and EGFR. Alternately, the plurality of capture agents comprises binding agents to CD9, CD63, CD81, PSMA, PCSA, B7H3, and/or EpCam. In yet other embodiments, the plurality of captures agents comprises binding agents to CD9, CD63, CD81, PSMA, PCSA, B7H3, EpCam, PSCA, ICAM, STEAP, and/or EGFR. The plurality of capture agents can also comprise a binding agent to TMEM211 and/or CD24.

The plurality of capture agents can also comprise one or more binding agents to vesicle biomarkers including CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, CD9, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, EGFR, B7H3, IC3b, MUC1, mesothelin, SPA, PCSA, CD63, STEAP, AQP5, CD81, DR3, PSM, GPCR, EphA2, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, A33, CD24, CD10, NGAL, EpCam, MUC17, TROP-2, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, and/or TNFR.

A subset of useful biomarker for capturing vesicles includes CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, and/or CD45. Another subset of useful biomarker for capturing vesicles includes CD10, NPGP/NPFF2, HER2/ERBB2, AGTR1, NPY1R, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, and/or NY-ESO-1. Still another subset of useful biomarker for capturing vesicles includes SPB, SPC, NSE, PGP9.5, CD9, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, EGFR, B7H3, IC3b, MUC1, mesothelin, SPA, PCSA, CD63, STEAP, AQP5, CD81, DR3, PSM, GPCR, EphA2, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, A33, CD24, CD10, NGAL, EpCam, MUC17, TROP-2, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, and/or TNFR.

The antibodies referenced herein can be immunoglobulin molecules or immunologically active portions of immunoglobulin molecules, i.e., molecules that contain an antigen binding site that specifically binds an antigen and synthetic antibodies. The immunoglobulin molecules can be of any class (e.g., IgG, IgE, IgM, IgD or IgA) or subclass of immunoglobulin molecule. Antibodies include, but are not limited to, polyclonal, monoclonal, bispecific, synthetic, humanized and chimeric antibodies, single chain antibodies, Fab fragments and F(ab′)₂ fragments, Fv or Fv′ portions, fragments produced by a Fab expression library, anti-idiotypic (anti-Id) antibodies, or epitope-binding fragments of any of the above. An antibody, or generally any molecule, “binds specifically” to an antigen (or other molecule) if the antibody binds preferentially to the antigen, and, e.g., has less than about 30%, 20%, 10%, 5% or 1% cross-reactivity with another molecule. In some embodiments, antibodies that cross react with multiple markers are used to bind vesicles. For example, an antibody that cross reacts with related members of a surface protein family can be used to bind vesicles displaying various members of that family.

The binding agent can also be a protein, polypeptide or peptide. The terms “polypeptide,” “peptide” and “protein” are used herein in their broadest sense and may include a sequence of subunit amino acids, amino acid analogs, or peptidomimetics. The subunits may be linked by peptide bonds. The polypeptides may be naturally occurring, processed forms of naturally occurring polypeptides (such as by enzymatic digestion), chemically synthesized or recombinantly expressed. The polypeptides for use in the methods of the present invention may be chemically synthesized using standard techniques. The polypeptides may comprise D-amino acids (which are resistant to L-amino acid-specific proteases), a combination of D- and L-amino acids, β amino acids, or various other designer or non-naturally occurring amino acids (e.g., β-methyl amino acids, Cα-methyl amino acids, and Nα-methyl amino acids, etc.) to convey special properties. Synthetic amino acids may include ornithine for lysine, and norleucine for leucine or isoleucine. In addition, the polypeptides can have peptidomimetic bonds, such as ester bonds, to prepare polypeptides with novel properties. For example, a polypeptide may be generated that incorporates a reduced peptide bond, i.e., R₁—CH₂—NH—R₂, where R₁ and R₂ are amino acid residues or sequences. A reduced peptide bond may be introduced as a dipeptide subunit. Such a polypeptide would be resistant to protease activity, and would possess an extended half-live in vivo. Polypeptides can also include peptoids (N-substituted glycines), in which the side chains are appended to nitrogen atoms along the molecule's backbone, rather than to the α-carbons, as in amino acids. The terms “polypeptides” and “peptides” are intended to be used interchangeably throughout this application, i.e. where the term peptide is used, it may also include polypeptides and where the term polypeptides is used, it may also include peptides. The term “protein” is also intended to be used interchangeably throughout this application with the terms “polypeptides” and “peptides” unless otherwise specified.

A vesicle may be isolated, captured or detected using a binding agent. The binding agent can be an agent that binds a vesicle “housekeeping protein,” or general vesicle biomarker. The biomarker can be CD63, CD9, CD81, CD82, CD37, CD53, Rab-5b, Annexin V or MFG-E8. Tetraspanins, a family of membrane proteins with four transmembrane domains, can be used as general vesicle markers. The tetraspanins include CD151, CD53, CD37, CD82, CD81, CD9 and CD63. There have been over 30 tetraspanins identified in mammals, including the TSPAN1 (TSP-1), TSPAN2 (TSP-2), TSPAN3 (TSP-3), TSPAN4 (TSP-4, NAG-2), TSPAN5 (TSP-5), TSPAN6 (TSP-6), TSPAN7 (CD231, TALLA-1, A15), TSPAN8 (CO-029), TSPAN9 (NET-5), TSPAN10 (Oculospanin), TSPAN11 (CD151-like), TSPAN12 (NET-2), TSPAN13 (NET-6), TSPAN14, TSPAN15 (NET-7), TSPAN16 (TM4-B), TSPAN17, TSPAN18, TSPAN19, TSPAN20 (UP1b, UPK1B), TSPAN21 (UP1a, UPK1A), TSPAN22 (RDS, PRPH2), TSPAN23 (ROM1), TSPAN24 (CD151), TSPAN25 (CD53), TSPAN26 (CD37), TSPAN27 (CD82), TSPAN28 (CD81), TSPAN29 (CD9), TSPAN30 (CD63), TSPAN31 (SAS), TSPAN32 (TSSC6), TSPAN33, and TSPAN34. Other commonly observed vesicle markers include those listed in Table 3. Any of these proteins can be used as vesicle markers.

TABLE 3 Proteins Observed in Vesicles from Multiple Cell Types Class Protein Antigen Presentation MHC class I, MHC class II, Integrins, Alpha 4 beta 1, Alpha M beta 2, Beta 2 Immunoglobulin family ICAM1/CD54, P-selection Cell-surface peptidases Dipeptidylpeptidase IV/CD26, Aminopeptidase n/CD13 Tetraspanins CD151, CD53, CD37, CD82, CD81, CD9 and CD63 Heat-shock proteins Hsp70, Hsp84/90 Cytoskeletal proteins Actin, Actin-binding proteins, Tubulin Membrane transport and Annexin I, Annexin II, Annexin IV, Annexin V, Annexin VI, fusion RAB7/RAP1B/RADGDI Signal transduction Gi2alpha/14-3-3, CBL/LCK Abundant membrane CD63, GAPDH, CD9, CD81, ANXA2, ENO1, SDCBP, MSN, MFGE8, EZR, proteins GK, ANXA1, LAMP2, DPP4, TSG101, HSPA1A, GDI2, CLTC, LAMP1, Cd86, ANPEP, TFRC, SLC3A2, RDX, RAP1B, RAB5C, RAB5B, MYH9, ICAM1, FN1, RAB11B, PIGR, LGALS3, ITGB1, EHD1, CLIC1, ATP1A1, ARF1, RAP1A, P4HB, MUC1, KRT10, HLA-A, FLOT1, CD59, C1orf58, BASP1, TACSTD1, STOM

The binding agent can also be an agent that binds to a vesicle derived from a specific cell type, such as a tumor cell (e.g. binding agent for Tissue factor, EpCam, B7H3 or CD24) or a specific cell-of-origin. The binding agent used to isolate or detect a vesicle can be a binding agent for an antigen selected from FIG. 1. The binding agent for a vesicle can also be selected from those listed in FIG. 2. The binding agent can be for an antigen such as a tetraspanin, MFG-E8, Annexin V, 5T4, B7H3, caveolin, CD63, CD9, E-Cadherin, Tissue factor, MFG-E8, TMEM211, CD24, PSCA, PCSA, PSMA, Rab-5B, STEAP, TNFR1, CD81, EpCam, CD59, CD81, ICAM, EGFR, or CD66. The binding agent can also be for a biomarker such as TMEM211 or CD24. The binding agent can also be for a biomarker such as CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, and/or TNFR. A binding agent for a platelet can be a glycoprotein such as GpIa-IIa, GpIIb-IIIa, GpIIIb, GpIb, or GpIX. One or more binding agents, such as one or more binding agents for two or more of the antigens, can be used for isolating or detecting a vesicle. The binding agent used can be selected based on the desire of isolating or detecting a vesicle derived from a particular cell type or cell-of-origin specific vesicle.

Integrins are receptors that mediate attachment between cells and surrounding tissues. Integrins work alongside other proteins such as cadherins, cell adhesion molecules and selectins to mediate cell-cell and cell-matrix interaction and communication. Integrins bind cell surface and extracellular matrix components such as fibronectin, vitronectin, collagen, and laminin. Integrins comprise heterodimers containing two distinct chains, called the α and β subunits. The mammalian α subunits include ITGA1 (CD49a, VLA1), ITGA2 (CD49b, VLA2), ITGA3 (CD49c, VLA3), ITGA4 (CD49d, VLA4), ITGA5 (CD49e, VLA5), ITGA6 (CD49f, VLA6), ITGA7 (FLJ25220), ITGA8, ITGA9 (RLC), ITGA10, ITGA11 (HsT18964), ITGAD (CD11D, FLJ39841), ITGAE (CD103, HUMINAE), ITGAL (CD11a, LFA1A), ITGAM (CD11b, MAC-1), ITGAV (CD51, VNRA, MSK8), ITGAW, and ITGAX (CD11c). The mammalian β subunits include ITGB1 (CD29, FNRB, MSK12, MDF20), ITGB2 (CD18, LFA-1, MAC-1, MFI7), ITGB3 (CD61, GP3A, GPIIIa), ITGB4 (CD104), ITGB5 (FLJ26658), ITGB6, ITGB7, and ITGB8. Through differential splicing of each subunit and different combinations of these α and β subunits, some 24 unique integrins have been detected in humans. Integrin levels can be assessed to characterize a cancer, such as a prostate or other cancer as described herein. In some embodiments, a method of characterizing a prostate cancer, e.g., to determine whether the cancer is indolent or aggressive, comprises assessing the levels of alpha2 beta1 integrin. Integrins can be assessed as vesicle surface markers or as internal vesicle payload, e.g., by detecting integrin mRNA.

A binding agent can also be linked directly or indirectly to a solid surface or substrate. A solid surface or substrate can be any physically separable solid to which a binding agent can be directly or indirectly attached including, but not limited to, surfaces provided by microarrays and wells, particles such as beads, columns, optical fibers, wipes, glass and modified or functionalized glass, quartz, mica, diazotized membranes (paper or nylon), polyformaldehyde, cellulose, cellulose acetate, paper, ceramics, metals, metalloids, semiconductive materials, quantum dots, coated beads or particles, other chromatographic materials, magnetic particles; plastics (including acrylics, polystyrene, copolymers of styrene or other materials, polypropylene, polyethylene, polybutylene, polyurethanes, TEFLON™, etc.), polysaccharides, nylon or nitrocellulose, resins, silica or silica-based materials including silicon and modified silicon, carbon, metals, inorganic glasses, plastics, ceramics, conducting polymers (including polymers such as polypyrole and polyindole); micro or nanostructured surfaces such as nucleic acid tiling arrays, nanotube, nanowire, or nanoparticulate decorated surfaces; or porous surfaces or gels such as methacrylates, acrylamides, sugar polymers, cellulose, silicates, or other fibrous or stranded polymers. In addition, as is known the art, the substrate may be coated using passive or chemically-derivatized coatings with any number of materials, including polymers, such as dextrans, acrylamides, gelatins or agarose. Such coatings can facilitate the use of the array with a biological sample.

For example, an antibody used to isolate a vesicle can be bound to a solid substrate such as a well, such as commercially available plates (e.g. from Nunc, Milan Italy). Each well can be coated with the antibody. In some embodiments, the antibody used to isolate a vesicle is bound to a solid substrate such as an array. The array can have a predetermined spatial arrangement of molecule interactions, binding islands, biomolecules, zones, domains or spatial arrangements of binding islands or binding agents deposited within discrete boundaries. Further, the term array may be used herein to refer to multiple arrays arranged on a surface, such as would be the case where a surface bore multiple copies of an array. Such surfaces bearing multiple arrays may also be referred to as multiple arrays or repeating arrays.

Arrays typically contain addressable moieties that can detect the presense of an entity, e.g., a vesicle in the sample via a binding event. An array may be referred to as a microarray. Arrays or microarrays include without limitation DNA microarrays, such as cDNA microarrays, oligonucleotide microarrays and SNP microarrays, microRNA arrays, protein microarrays, antibody microarrays, tissue microarrays, cellular microarrays (also called transfection microarrays), chemical compound microarrays, and carbohydrate arrays (glycoarrays). DNA arrays typically comprise addressable nucleotide sequences that can bind to sequences present in a sample. MicroRNA arrays, e.g., the MMChips array from the University of Louisville or commercial systems from Agilent, can be used to detect microRNAs. Protein microarrays can be used to identify protein-protein interactions, including without limitation identifying substrates of protein kinases, transcription factor protein-activation, or to identify the targets of biologically active small molecules. Protein arrays may comprise an array of different protein molecules, commonly antibodies, or nucleotide sequences that bind to proteins of interest. In a non-limiting example, a protein array can be used to detect vesicles having certain proteins on their surface. Antibody arrays comprise antibodies spotted onto the protein chip that are used as capture molecules to detect proteins or other biological materials from a sample, e.g., from cell or tissue lysate solutions. For example, antibody arrays can be used to detect vesicle-associated biomarkers from bodily fluids, e.g., serum or urine. Tissue microarrays comprise separate tissue cores assembled in array fashion to allow multiplex histological analysis. Cellular microarrays, also called transfection microarrays, comprise various capture agents, such as antibodies, proteins, or lipids, which can interact with cells to facilitate their capture on addressable locations. Cellular arrays can also be used to capture vesicles due to the similarity between a vesicle and cellular membrane. Chemical compound microarrays comprise arrays of chemical compounds and can be used to detect protein or other biological materials that bind the compounds. Carbohydrate arrays (glycoarrays) comprise arrays of carbohydrates and can detect, e.g., protein that bind sugar moieties. One of skill will appreciate that similar technologies or improvements can be used according to the methods of the invention.

A binding agent can also be bound to particles such as beads or microspheres. For example, an antibody specific for a component of a vesicle can be bound to a particle, and the antibody-bound particle is used to isolate a vesicle from a biological sample. In some embodiments, the microspheres may be magnetic or fluorescently labeled. In addition, a binding agent for isolating vesicles can be a solid substrate itself. For example, latex beads, such as aldehyde/sulfate beads (Interfacial Dynamics, Portland, Oreg.) can be used.

A binding agent bound to a magnetic bead can also be used to isolate a vesicle. For example, a biological sample such as serum from a patient can be collected for colon cancer screening. The sample can be incubated with anti-CCSA-3 (Colon Cancer-Specific Antigen) coupled to magnetic microbeads. A low-density microcolumn can be placed in the magnetic field of a MACS Separator and the column is then washed with a buffer solution such as Tris-buffered saline. The magnetic immune complexes can then be applied to the column and unbound, non-specific material can be discarded. The CCSA-3 selected vesicle can be recovered by removing the column from the separator and placing it on a collection tube. A buffer can be added to the column and the magnetically labeled vesicle can be released by applying the plunger supplied with the column. The isolated vesicle can be diluted in IgG elution buffer and the complex can then be centrifuged to separate the microbeads from the vesicle. The pelleted isolated cell-of-origin specific vesicle can be resuspended in buffer such as phosphate-buffered saline and quantitated. Alternatively, due to the strong adhesion force between the antibody captured cell-of-origin specific vesicle and the magnetic microbeads, a proteolytic enzyme such as trypsin can be used for the release of captured vesicles without the need for centrifugation. The proteolytic enzyme can be incubated with the antibody captured cell-of-origin specific vesicles for at least a time sufficient to release the vesicles.

A binding agent, such as an antibody, for isolating vesicles is preferably contacted with the biological sample comprising the vesicles of interest for a time sufficient for the binding agent to bind to a component of the vesicle. In one embodiment, an antibody is contacted with a biological sample for various intervals ranging from seconds to days, including but not limited to, about 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes, 10 minutes, 15 minutes, 20 minutes, 25 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, 5 hours, 7 hours, 10 hours, 15 hours, 1 day, 3 days, 7 days or 10 days. The time can be selected to provide for efficient binding without allowing degradation of the binding agent system or vesicles.

A binding agent, such as an antibody specific to an antigen listed in FIG. 1, or a binding agent listed in FIG. 2, can be labeled to allow for its detection. Appropriate labels include without limitation a magnetic label, a fluorescent moiety, an enzyme, a chemiluminescent probe, a metal particle, a non-metal colloidal particle, a polymeric dye particle, a pigment molecule, a pigment particle, an electrochemically active species, semiconductor nanocrystal or other nanoparticles including quantum dots or gold particles, fluorophores, quantum dots, or radioactive labels. Protein labels include green fluorescent protein (GFP) and variants thereof (e.g., cyan fluorescent protein and yellow fluorescent protein); and luminescent proteins such as luciferase, as described below. Radioactive labels include without limitation radioisotopes (radionuclides), such as ³H, ¹¹C, ¹⁴C, ¹⁸F, ³²F, ³⁵S, ⁶⁴Cu, ⁶⁸Ga, ⁸⁶Y, ⁹⁹Tc, ¹¹¹In, ¹²³I, ¹²⁴I, ¹²⁵I, ¹³¹I, ¹³³Xe, ¹⁷⁷Lu, ²¹¹At, or ²¹³Bi. Fluorescent labels include without limitation a rare earth chelate (e.g., europium chelate), rhodamine; fluorescein types including without limitation FITC, 5-carboxyfluorescein, 6-carboxy fluorescein; a rhodamine type including without limitation TAMRA; dansyl; Lissamine; cyanines; phycoerythrins; Texas Red; Cy3, Cy5, dapoxyl, NBD, Cascade Yellow, dansyl, PyMPO, pyrene, 7-diethylaminocoumarin-3-carboxylic acid and other coumarin derivatives, Marina Blue™, Pacific Blue™, Cascade Blue™, 2-anthracenesulfonyl, PyMPO, 3,4,9,10-perylene-tetracarboxylic acid, 2,7-difluorofluorescein (Oregon Green™ 488-X), 5-carboxyfluorescein, Texas Red™-X, Alexa Fluor 430, 5-carboxytetramethylrhodamine (5-TAMRA), 6-carboxytetramethylrhodamine (6-TAMRA), BODIPY FL, bimane, and Alexa Fluor 350, 405, 488, 500, 514, 532, 546, 555, 568, 594, 610, 633, 647, 660, 680, 700, and 750, and derivatives thereof, among many others. See, e.g., “The Handbook—A Guide to Fluorescent Probes and Labeling Technologies,” Tenth Edition, available on the interne at probes (dot) invitrogen (dot) com/handbook.

A binding agent can be directly, e.g., via a covalent bond. Binding agents can also be indirectly labeled, such as when a label is attached to the binding agent through a binding system. In a non-limiting example, consider an antibody labeled through biotin-streptavidin. Alternatively, an antibody is not labeled, but is later contacted with a second antibody that is labeled after the first antibody is bound to an antigen of interest.

For example, various enzyme-substrate labels are available or disclosed (see for example, U.S. Pat. No. 4,275,149). The enzyme generally catalyzes a chemical alteration of a chromogenic substrate that can be measured using various techniques. For example, the enzyme may catalyze a color change in a substrate, which can be measured spectrophotometrically. Alternatively, the enzyme may alter the fluorescence or chemiluminescence of the substrate. Examples of enzymatic labels include luciferases (e.g., firefly luciferase and bacterial luciferase; U.S. Pat. No. 4,737,456), luciferin, 2,3-dihydrophthalazinediones, malate dehydrogenase, urease, peroxidase such as horseradish peroxidase (HRP), alkaline phosphatase (AP), β-galactosidase, glucoamylase, lysozyme, saccharide oxidases (e.g., glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase), heterocyclic oxidases (such as uricase and xanthine oxidase), lactoperoxidase, microperoxidase, and the like. Examples of enzyme-substrate combinations include, but are not limited to, horseradish peroxidase (HRP) with hydrogen peroxidase as a substrate, wherein the hydrogen peroxidase oxidizes a dye precursor (e.g., orthophenylene diamine (OPD) or 3,3′,5,5′-tetramethylbenzidine hydrochloride (TMB)); alkaline phosphatase (AP) with para-nitrophenyl phosphate as chromogenic substrate; and β-D-galactosidase (β-D-Gal) with a chromogenic substrate (e.g., p-nitrophenyl-β-D-galactosidase) or fluorogenic substrate 4-methylumbelliferyl-β-D-galactosidase.

Depending on the method of isolation or detection used, the binding agent may be linked to a solid surface or substrate, such as arrays, particles, wells and other substrates described above. Methods for direct chemical coupling of antibodies, to the cell surface are known in the art, and may include, for example, coupling using glutaraldehyde or maleimide activated antibodies. Methods for chemical coupling using multiple step procedures include biotinylation, coupling of trinitrophenol (TNP) or digoxigenin using for example succinimide esters of these compounds. Biotinylation can be accomplished by, for example, the use of D-biotinyl-N-hydroxysuccinimide. Succinimide groups react effectively with amino groups at pH values above 7, and preferentially between about pH 8.0 and about pH 8.5. Biotinylation can be accomplished by, for example, treating the cells with dithiothreitol followed by the addition of biotin maleimide.

Flow Cytometry

Isolation or detection of a vesicle using a particle such as a bead or microsphere can also be performed using flow cytometry. Flow cytometry can be used for sorting microscopic particles suspended in a stream of fluid. As particles pass through they can be selectively charged and on their exit can be deflected into separate paths of flow. It is therefore possible to separate populations from an original mix, such as a biological sample, with a high degree of accuracy and speed. Flow cytometry allows simultaneous multiparametric analysis of the physical and/or chemical characteristics of single cells flowing through an optical/electronic detection apparatus. A beam of light, usually laser light, of a single frequency (color) is directed onto a hydrodynamically focused stream of fluid. A number of detectors are aimed at the point where the stream passes through the light beam; one in line with the light beam (Forward Scatter or FSC) and several perpendicular to it (Side Scatter or SSC) and one or more fluorescent detectors.

Each suspended particle passing through the beam scatters the light in some way, and fluorescent chemicals in the particle may be excited into emitting light at a lower frequency than the light source. This combination of scattered and fluorescent light is picked up by the detectors, and by analyzing fluctuations in brightness at each detector (one for each fluorescent emission peak), it is possible to deduce various facts about the physical and chemical structure of each individual particle. FSC correlates with the cell size and SSC depends on the inner complexity of the particle, such as shape of the nucleus, the amount and type of cytoplasmic granules or the membrane roughness. Some flow cytometers have eliminated the need for fluorescence and use only light scatter for measurement.

Flow cytometers can analyze several thousand particles every second in “real time” and can actively separate out and isolate particles having specified properties. They offer high-throughput automated quantification, and separation, of the set parameters for a high number of single cells during each analysis session. Flow cytomers can have multiple lasers and fluorescence detectors, allowing multiple labels to be used to more precisely specify a target population by their phenotype. Thus, a flow cytometer, such as a multicolor flow cytometer, can be used to detect one or more vesicles with multiple fluorescent labels or colors. In some embodiments, the flow cytometer can also sort or isolate different vesicle populations, such as by size or by different markers.

The flow cytometer may have one or more lasers, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more lasers. In some embodiments, the flow cytometer can detect more than one color or fluorescent label, such as at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 different colors or fluorescent labels. For example, the flow cytometer can have at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 fluorescence detectors.

Examples of commercially available flow cytometers that can be used to detect or analyze one or more vesicles, to sort or separate different populations of vesicles, include, but are not limited to the MoFlo™ XDP Cell Sorter (Beckman Coulter, Brea, Calif.), MoFlo™ Legacy Cell Sorter (Beckman Coulter, Brea, Calif.), BD FACSAria™ Cell Sorter (BD Biosciences, San Jose, Calif.), BD™ LSRII (BD Biosciences, San Jose, Calif.), and BD FACSCalibur™ (BD Biosciences, San Jose, Calif.). Use of multicolor or multi-fluor cytometers can be used in multiplex analysis of vesicles, as further described below. In some embodiments, the flow cytometer can sort, and thereby collect or sort more than one population of vesicles based one or more characteristics. In embodiments wherein different populations of vesicles differ in size, vesicles within each population can be differentially detected or sorted based on size. In another embodiment, two different populations of vesicles are differentially labeled to allow for detection or sorting. Size and label can be used together for detection and sorting.

The data resulting from flow-cytometers can be plotted in 1 dimension to produce histograms or seen in 2 dimensions as dot plots or in 3 dimensions with newer software. The regions on these plots can be sequentially separated by a series of subset extractions which are termed gates. Specific gating protocols exist for diagnostic and clinical purposes especially in relation to hematology. The plots are often made on logarithmic scales. Because different fluorescent dye's emission spectra overlap, signals at the detectors have to be compensated electronically as well as computationally. Fluorophores for labeling biomarkers may include those described in Ormerod, Flow Cytometry 2nd ed., Springer-Verlag, New York (1999), and in Nida et al., Gynecologic Oncology 2005; 4 889-894 which is incorporated herein by reference.

Multiplexing

Multiplex experiments comprise experiments that can simultaneously measure multiple analytes in a single assay. Vesicles and associated biomarkers can be assessed in a multiplex fashion. Different binding agents can be used for multiplexing different vesicle populations. Different vesicle populations can be isolated or detected using different binding agents such as those disclosed herein. Different binding agents can be used for multiplexing different vesicle populations. Each population in a biological sample can be labeled with a different label, such as a fluorophore, quantum dot, or radioactive label, such as described above. The label can be directly conjugated to a binding agent or indirectly used to detect a binding agent that binds a vesicle. The number of populations detected in a multiplexing assay is dependent on the resolution capability of the labels and the summation of signals, as more than two differentially labeled vesicle populations that bind two or more affinity elements can produce summed signals.

Multiplexing can be performed simultaneously on multiple vesicle populations. Multiplex analysis of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75 or 100 different vesicle populations may be performed. For example, one population of vesicles specific to a cell-of-origin can be assayed along with a second population of vesicles specific to a different cell-of-origin, where each population is labeled with a different label. Alternatively, a population of vesicles with a particular biomarker or biosignature can be assayed along with a second population of vesicles with a different biomarker or biosignature. In some cases, hundreds or thousands of vesicles are assessed in a single assay.

In one embodiment, multiplex analysis is performed by applying a plurality of vesicles comprising more than one population of vesicles to a plurality of substrates, such as beads. Each bead is coupled to one or more capture agents. The plurality of beads is divided into subsets, where beads with the same capture agent or combination of capture agents form a subset of beads, such that each subset of beads has a different capture agent or combination of capture agents than another subset of beads. The beads can then be used to capture vesicles that comprise a component that binds to the capture agent. The different subsets can be used to capture different populations of vesicles. The captured vesicles can then be analyzed by detecting one or more biomarkers.

Flow cytometry can be used in combination with a particle-based or bead based assay. Multiparametric immunoassays or other high throughput detection assays using bead coated with cognate ligands and reporter molecules with specific activities consistent with high sensitivity automation can be used. For example, beads in each subset can be differentially labeled from another subset. In a particle based assay system, a binding agent or capture agent for a vesicle, such as a capture antibody, can be immobilized on addressable beads or microspheres. Each binding agent for each individual binding assay (such as an immunoassay when the binding agent is an antibody) can be coupled to a distinct type of microsphere (i.e., microbead) and the binding assay reaction takes place on the surface of the microspheres. Microspheres can be distinguished by different labels, for example, a microsphere with a specific capture agent would have a different signaling label as compared to another microsphere with a different capture agent. For example, microspheres can be dyed with discrete fluorescence intensities such that the fluorescence intensity of a microsphere with a specific binding agent is different than that of another microsphere with a different binding agent. Vesicles bound by the different capture agents can be detected using the differing labels.

A microsphere can be labeled or dyed with at least 2 different labels or dyes. In some embodiments, the microsphere is labeled with at least 3, 4, 5, 6, 7, 8, 9, or 10 different labels. Different microspheres in a plurality of microspheres can have more than one label or dye, wherein various subsets of the microspheres have various ratios and combinations of the labels or dyes permitting detection of different microspheres with different binding agents. For example, the various ratios and combinations of labels and dyes can permit different fluorescent intensities. Alternatively, the various ratios and combinations maybe used to generate different detection patters to identify the binding agent. The microspheres can be labeled or dyed externally or may have intrinsic fluorescence or signaling labels. Beads can be loaded separately with their appropriate binding agents and thus, different vesicle populations can be isolated based on the different binding agents on the differentially labeled microspheres to which the different binding agents are coupled.

In another embodiment, multiplex analysis can be performed using a planar substrate, wherein the substrate comprises a plurality of capture agents. The plurality of capture agents can capture one or more populations of vesicles, and one or more biomarkers of the captured vesicles detected. The planar substrate can be a microarray or other substrate as further described herein.

Binding Agents

A vesicle may be isolated or detected using a binding agent for a novel component of a vesicle, such as an antibody for an antigen specific to a vesicle of interest. Novel antigens that are specific to a vesicle of interest may be isolated or identified using different test compounds of known composition bound to a substrate, such as an array or a plurality of particles, which can allow a large amount of chemical/structural space to be adequately sampled using only a small fraction of the space. The novel antigen identified can also serve as a biomarker for the vesicle. For example, a novel antigen identified for a cell-of-origin specific vesicle can be a useful biomarker for detecting that vesicle population.

The term “agent” or “reagent” as used in respect to contacting a sample can mean any entity designed to bind, hybridize, associate with or otherwise detect or facilitate detection of a target molecule, including target polypeptides, peptides, nucleic acid molecules, leptins, lipids, or any other biological entity that can be detected as described herein or as known in the art. Examples of such agents/reagents are well known in the art, and include but are not limited to universal or specific nucleic acid primers, nucleic acid probes, antibodies, aptamers, peptoid, peptide nucleic acid, locked nucleic acid, lectin, dendrimer, chemical compound, or other entities described herein or known in the art.

A binding agent can be identified by screening either a homogeneous or heterogeneous vesicle population against test compounds. Since the composition of each test compound on the substrate surface is known, this constitutes a screen for affinity elements. For example, a test compound array comprises test compounds at specific locations on the substrate addressable locations. Vesicles can be contacted with the array to determine which of the addressable compounds can be used to identify one or more binding agents for the desired vesicles. The test compounds can all be unrelated or related based on minor variations of a core sequence or structure. The different test compounds may include variants of a given test compound (such as polypeptide isoforms), test compounds that are structurally or compositionally unrelated, or a combination thereof.

A test compound can be a peptoid, polysaccharide, organic compound, inorganic compound, polymer, lipids, nucleic acid, polypeptide, antibody, protein, polysaccharide, or other compound that can be used as a binding agent. The test compound can be natural or synthetic. The test compound can comprise or consist of linear or branched heteropolymeric compounds based on any of a number of linkages or combinations of linkages (e.g., amide, ester, ether, thiol, radical additions, metal coordination, etc.), dendritic structures, circular structures, cavity structures or other structures with multiple nearby sites of attachment that serve as scaffolds upon which specific additions are made. Thes test compound can be spotted on a substrate or synthesized in situ, using standard methods in the art. In addition, the test compound can be spotted or synthesized in situ in combinations in order to detect useful interactions, such as cooperative binding.

The test compound can be a polypeptide with known amino acid sequence, thus, detection of a test compound binding with a vesicle can lead to identification of a polypeptide of known amino sequence that can be used as a binding agent. For example, a homogenous population of vesicles can be applied to a spotted array on a slide containing between a few and 1,000,000 test polypeptides having a length of variable amino acids. The polypeptides can be attached to the surface through the C-terminus. The sequence of the polypeptides can be generated randomly from 19 amino acids, excluding cysteine. The binding reaction can include a non-specific competitor, such as excess bacterial proteins labeled with another dye such that the specificity ratio for each polypeptide binding target can be determined. The polypeptides with the highest specificity and binding can be selected. The identity of the polypeptide on each spot is known, and thus can be readily identified. Once the novel antigens specific to the homogeneous vesicle population, such as a cell-of-origin specific vesicle is identified, such cell-of-origin specific vesicles may subsequently be isolated using such antigens in methods described hereafter.

An array can also be used for identifying an antibody as a binding agent for a vesicle. Test antibodies can be attached to an array and screened against a heterogeneous population of vesicles to identify antibodies that can be used to isolate or identify a vesicle. A homogeneous population of vesicles such as cell-of-origin specific vesicles can also be screened with an antibody array. Other than identifying antibodies to isolate or detect a homogeneous population of vesicles, one or more protein biomarkers specific to the homogenous population can be identified. Commercially available platforms with test antibodies pre-selected or custom selection of test antibodies attached to the array can be used. For example, an antibody array from Full Moon Biosystems can be screened using prostate cancer cell derived vesicles identifying antibodies to Bcl-XL, ERCC1, Keratin 15, CD81/TAPA-1, CD9, Epithelial Specific Antigen (ESA), and Mast Cell Chymase as binding agents (see for example, FIG. 62), and the proteins identified can be used as biomarkers for the vesicles.

An antibody or synthetic antibody to be used as a binding agent can also be identified through a peptide array. Another method is the use of synthetic antibody generation through antibody phage display. M13 bacteriophage libraries of antibodies (e.g. Fabs) are displayed on the surfaces of phage particles as fusions to a coat protein. Each phage particle displays a unique antibody and also encapsulates a vector that contains the encoding DNA. Highly diverse libraries can be constructed and represented as phage pools, which can be used in antibody selection for binding to immobilized antigens. Antigen-binding phages are retained by the immobilized antigen, and the nonbinding phages are removed by washing. The retained phage pool can be amplified by infection of an Escherichia coli host and the amplified pool can be used for additional rounds of selection to eventually obtain a population that is dominated by antigen-binding clones. At this stage, individual phase clones can be isolated and subjected to DNA sequencing to decode the sequences of the displayed antibodies. Through the use of phase display and other methods known in the art, high affinity designer antibodies for vesicles can be generated.

Bead-based assays can also be used to identify novel binding agents to isolate or detect a vesicle. A test antibody or peptide can be conjugated to a particle. For example, a bead can be conjugated to an antibody or peptide and used to detect and quantify the proteins expressed on the surface of a population of vesicles in order to discover and specifically select for novel antibodies that can target vesicles from specific tissue or tumor types. Any molecule of organic origin can be successfully conjugated to a polystyrene bead through use of a commercially available kit according to manufacturer's instructions. Each bead set can be colored a certain detectable wavelength and each can be linked to a known antibody or peptide which can be used to specifically measure which beads are linked to exosomal proteins matching the epitope of previously conjugated antibodies or peptides. The beads can be dyed with discrete fluorescence intensities such that each bead with a different intensity has a different binding agent as described above.

For example, a purified vesicle preparation can be diluted in assay buffer to an appropriate concentration according to empirically determined dynamic range of assay. A sufficient volume of coupled beads can be prepared and approximately 1 μl of the antibody-coupled beads can be aliqouted into a well and adjusted to a final volume of approximately 50 μl. Once the antibody-conjugated beads have been added to a vacuum compatible plate, the beads can be washed to ensure proper binding conditions. An appropriate volume of vesicle preparation can then be added to each well being tested and the mixture incubated, such as for 15-18 hours. A sufficient volume of detection antibodies using detection antibody diluent solution can be prepared and incubated with the mixture for 1 hour or for as long as necessary. The beads can then be washed before the addition of detection antibody (biotin expressing) mixture composed of streptavidin phycoereythin. The beads can then be washed and vacuum aspirated several times before analysis on a suspension array system using software provided with an instrument. The identity of antigens that can be used to selectively extract the vesicles can then be elucidated from the analysis.

Assays using imaging systems can be utilized to detect and quantify proteins expressed on the surface of a vesicle in order to discover and specifically select for and enrich vesicles from specific tissue, cell or tumor types. Antibodies, peptides or cells conjugated to multiple well multiplex carbon coated plates can be used. Simultaneous measurement of many analytes in a well can be achieved through the use of capture antibodies arrayed on the patterned carbon working surface. Analytes can then be detected with antibodies labeled with reagents in electrode wells with an enhanced electro-chemiluminescent plate. Any molecule of organic origin can be successfully conjugated to the carbon coated plate. Proteins expressed on the surface of vesicles can be identified from this assay and can be used as targets to specifically select for and enrich vesicles from specific tissue or tumor types.

The binding agent can also be an aptamer, which refers to nucleic acids that can bond molecules other than their complementary sequence. An aptamer typically contains 30-80 nucleic acids and can have a high affinity towards a certain target molecule (K_(d)'s reported are between 10⁻¹¹-10⁻⁶ mole/1). An aptamer for a target can be identified using systematic evolution of ligands by exponential enrichment (SELEX) (Tuerk & Gold, Science 249:505-510, 1990; Ellington & Szostak, Nature 346:818-822, 1990), such as described in U.S. Pat. Nos. 5,270,163, 6,482, 594, 6,291, 184, 6,376,190 and U.S. Pat. No. 6,458,539. A library of nucleic acids can be contacted with a target vesicle, and those nucleic acids specifically bound to the target are partitioned from the remainder of nucleic acids in the library which do not specifically bind the target. The partitioned nucleic acids are amplified to yield a ligand-enriched pool. Multiple cycles of binding, partitioning, and amplifying (i.e., selection) result in identification of one or more aptamers with the desired activity. Another method for identifying an aptamer to isolate vesicles is described in U.S. Pat. No. 6,376,190, which describes increasing or decreasing frequency of nucleic acids in a library by their binding to a chemically synthesized peptide. Modified methods, such as Laser SELEX or deSELEX as described in U.S. Patent Publication No. 20090264508 can also be used.

The term “specific” as used herein in regards to a binding agent can mean that an agent has a greater affinity for its target than other targets, typically with a much great affinity, but does not require that the binding agent is absolutely specific for its target.

Microfluidics

Microfluidic devices can be used for carrying out methods for isolating or identifying vesicles as described herein. The methods of isolating or detecting a vesicle, such as described herien, can be performed using a microfluidic device. Microfluidic devices, which may also be referred to as “lab-on-a-chip” systems, biomedical micro-electro-mechanical systems (bioMEMs), or multicomponent integrated systems, can be used for isolating and analyzing a vesicle. Such systems miniaturize and compartmentalize processes that allow for binding of vesicles, detection of biosignatures, and other processes.

A microfluidic device can also be used for isolation of a vesicle through size differential or affinity selection. For example, a microfluidic device can use one more channels for isolating a vesicle from a biological sample based on size or by using one or more binding agents for isolating a vesicle from a biological sample. A biological sample can be introduced into one or more microfluidic channels, which selectively allows the passage of a vesicle. The selection can be based on a property of the vesicle, such as the size, shape, deformability, or biosignature of the vesicle.

In one embodiment, a heterogeneous population of vesicles can be introduced into a microfluidic device, and one or more different homogeneous populations of vesicles can be obtained. For example, different channels can have different size selections or binding agents to select for different vesicle populations. Thus, a microfluidic device can isolate a plurality of vesicles wherein at least a subset of the plurality of vesicles comprises a different biosignature from another subset of the plurality of vesicles. For example, the microfluidic device can isolate at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, or 100 different subsets of vesicles, wherein each subset of vesicles comprises a different biosignature.

In some embodiments, the microfluidic device can comprise one or more channels that permit further enrichment or selection of a vesicle. A population of vesicles that has been enriched after passage through a first channel can be introduced into a second channel, which allows the passage of the desired vesicle or vesicle population to be further enriched, such as through one or more binding agents present in the second channel.

Array-based assays and bead-based assays can be used with microfluidic device. For example, the binding agent can be coupled to beads and the binding reaction between the beads and vesicle can be performed in a microfluidic device. Multiplexing can also be performed using a microfluidic device. Different compartments can comprise different binding agents for different populations of vesicles, where each population is of a different cell-of-origin specific vesicle population. In one embodiment, each population has a different biosignature. The hybridization reaction between the microsphere and vesicle can be performed in a microfluidic device and the reaction mixture can be delivered to a detection device. The detection device, such as a dual or multiple laser detection system can be part of the microfluidic system and can use a laser to identify each bead or microsphere by its color-coding, and another laser can detect the hybridization signal associated with each bead.

Any appropriate microfluidic device can be used in the methods of the invention. Examples of microfluidic devices that may be used, or adapted for use with vesicles, include but are not limited to those described in U.S. Pat. Nos. 7,591,936, 7,581,429, 7,579,136, 7,575,722, 7,568,399, 7,552,741, 7,544,506, 7,541,578, 7,518,726, 7,488,596, 7,485,214, 7,467,928, 7,452,713, 7,452,509, 7,449,096, 7,431,887, 7,422,725, 7,422,669, 7,419,822, 7,419,639, 7,413,709, 7,411,184, 7,402,229, 7,390,463, 7,381,471, 7,357,864, 7,351,592, 7,351,380, 7,338,637, 7,329,391, 7,323,140, 7,261,824, 7,258,837, 7,253,003, 7,238,324, 7,238,255, 7,233,865, 7,229,538, 7,201,881, 7,195,986, 7,189,581, 7,189,580, 7,189,368, 7,141,978, 7,138,062, 7,135,147, 7,125,711, 7,118,910, 7,118,661, 7,640,947, 7,666,361, 7,704,735; and International Patent Publication WO 2010/072410; each of which patents or applications are incorporated herein by reference in their entirety. Another example for use with methods disclosed herein is described in Chen et al., “Microfluidic isolation and transcriptome analysis of serum vesicles,” Lab on a Chip, Dec. 8, 2009 DOI: 10.1039/b916199f.

Other microfluidic devices for use with the invention include devices comprising elastomeric layers, valves and pumps, including without limitation those disclosed in U.S. Pat. Nos. 5,376,252, 6,408,878, 6,645,432, 6,719,868, 6,793,753, 6,899,137, 6,929,030, 7,040,338, 7,118,910, 7,144,616, 7,216,671, 7,250,128, 7,494,555, 7,501,245, 7,601,270, 7,691,333, 7,754,010, 7,837,946; U.S. Patent Application Nos. 2003/0061687, 2005/0084421, 2005/0112882, 2005/0129581, 2005/0145496, 2005/0201901, 2005/0214173, 2005/0252773, 2006/0006067; and EP Patent Nos. 0527905 and 1065378; each of which application is herein incorporated by reference. In some instances, much or all of the devices are composed of elastomeric material. Certain devices are designed to conduct thermal cycling reactions (e.g., PCR) with devices that include one or more elastomeric valves to regulate solution flow through the device. The devices can comprise arrays of reaction sites thereby allowing a plurality of reactions to be performed. Thus, the devices can be used to assess circulating microRNAs in a multiplex fashion, including microRNAs isolated from vesicles. In an embodiment, the microfluidic device comprises (a) a first plurality of flow channels formed in an elastomeric substrate; (b) a second plurality of flow channels formed in the elastomeric substrate that intersect the first plurality of flow channels to define an array of reaction sites, each reaction site located at an intersection of one of the first and second flow channels; (c) a plurality of isolation valves disposed along the first and second plurality of flow channels and spaced between the reaction sites that can be actuated to isolate a solution within each of the reaction sites from solutions at other reaction sites, wherein the isolation valves comprise one or more control channels that each overlay and intersect one or more of the flow channels; and (d) means for simultaneously actuating the valves for isolating the reaction sites from each other. Various modifications to the basic structure of the device are envisioned within the scope of the invention. MicroRNAs can be detected in each of the reaction sites by using PCR methods. For example, the method can comprise the steps of the steps of: (i) providing a microfluidic device, the microfluidic device comprising: a first fluidic channel having a first end and a second end in fluid communication with each other through the channel; a plurality of flow channels, each flow channel terminating at a terminal wall; wherein each flow channel branches from and is in fluid communication with the first fluidic channel, wherein an aqueous fluid that enters one of the flow channels from the first fluidic channel can flow out of the flow channel only through the first fluidic channel; and, an inlet in fluid communication with the first fluidic channel, the inlet for introducing a sample fluid; wherein each flow channel is associated with a valve that when closed isolates one end of the flow channel from the first fluidic channel, whereby an isolated reaction site is formed between the valve and the terminal wall; a control channel; wherein each the valve is a deflectable membrane which is deflected into the flow channel associated with the valve when an actuating force is applied to the control channel, thereby closing the valve; and wherein when the actuating force is applied to the control channel a valve in each of the flow channels is closed, so as to produce the isolated reaction site in each flow channel; (ii) introducing the sample fluid into the inlet, the sample fluid filling the flow channels; (iii) actuating the valve to separate the sample fluid into the separate portions within the flow channels; (iv) amplifying the nucleic acid in the sample fluid; (v) analyzing the portions of the sample fluid to determine whether the amplifying produced the reaction. The sample fluid can contain an amplifiable nucleic acid target, e.g., a microRNA, and the conditions can be polymerase chain reaction (PCR) conditions, so that the reaction results in a PCR product being formed.

In an embodiment, the PCR used to detect microRNA is digital PCR, which is described by Brown, et al., U.S. Pat. No. 6,143,496, titled “Method of sampling, amplifying and quantifying segment of nucleic acid, polymerase chain reaction assembly having nanoliter-sized chambers and methods of filling chambers”, and by Vogelstein, et al, U.S. Pat. No. 6,446,706, titled “Digital PCR”, both of which are hereby incorporated by reference in their entirety. In digital PCR, a sample is partitioned so that individual nucleic acid molecules within the sample are localized and concentrated within many separate regions, such as the reaction sites of the microfluidic device described above. The partitioning of the sample allows one to count the molecules by estimating according to Poisson. As a result, each part will contain “0” or “1” molecules, or a negative or positive reaction, respectively. After PCR amplification, nucleic acids may be quantified by counting the regions that contain PCR end-product, positive reactions. In conventional PCR, starting copy number is proportional to the number of PCR amplification cycles. Digital PCR, however, is not dependent on the number of amplification cycles to determine the initial sample amount, eliminating the reliance on uncertain exponential data to quantify target nucleic acids and providing absolute quantification. Thus, the method can provide a sensitive approach to detecting microRNAs in a sample.

In one embodiment, a microfluidic device for isolating or detecting a vesicle comprises a channel of less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 55, of 60 mm in width, or between about 2-60, 3-50, 3-40, 3-30, 3-20, or 4-20 mm in width. The microchannel can have a depth of less than about 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 45, 50, 55, 60, 65 or 70 mm, or between about 10-70, 10-40, 15-35, or 20-30 mm. Furthermore, the microchannel can have a length of less than about 1, 2, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5 or 10 cm. The microfluidic device can have grooves on its ceiling that are less than about 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 6, 65, 70, 75, or 80 mm wide, or between about 40-80, 40-70, 40-60 or 45-55 mm wide. The grooves can be less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, or 50 mm deep, such as between about 1-50, 5-40, 5-30, 3-20 or 5-15 μm.

The microfluidic device can have one or more binding agents attached to a surface in a channel, or present in a channel. For example, the microchannel can have one or more capture agents, such as a capture agent for EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP, and/or EGFR. The capture agent can also be for TMEM211 and/or CD24. In other embodiments, the one or more capture agents recognizes one or more of: CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, and TNFR. In an embodiment, a microchannel surface is treated with avidin and a capture agent, such as an antibody, that is biotinylated can be injected into the channel to bind the avidin. In other embodiments, the capture agents are present in chambers or other components of a microfluidic device. The capture agents can also be attached to beads that can be manipulated to move through the microfluidic channels. In one embodiment, the capture agents are attached to magnetic beads. The beads can be manipulated using magnets.

A biological sample can be flowed into the microfluidic device, or a microchannel, at rates such as at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, or 50 μl per minute, such as between about 1-50, 5-40, 5-30, 3-20 or 5-15 μl per minute. One or more vesicles can be captured and directly detected in the microfluidic device. Alternatively, the captured vesicle may be released and exit the microfluidic device prior to analysis. In another embodiment, one or more captured vesicles are lysed in the microchannel and the lysate can be analyzed, e.g., to examine payload with the vesicles. Lysis buffer can be flowed through the channel and lyse the captured vesicles. For example, the lysis buffer can be flowed into the device or microchannel at rates such as at least about a, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 26, 27, 28, 29, 30, 35, 40, 45, or 50 μl per minute, such as between about 1-50, 5-40, 10-30, 5-30 or 10-35 μl per minute. The lysate can be collected and analyzed, such as performing RT-PCR, PCR, mass spectrometry, Western blotting, or other assays, to detect one or more biomarkers of the vesicle.

The various isolation and detection systems described herein can be used to isolate or detect vesicles that are informative for diagnosis, prognosis, disease stratification, theranosis, prediction of responder/non-responder status, disease monitoring, treatment monitoring and the like as related to such diseases and disorders. Combinations of the isolation techniques are within the scope of the invention. In a non-limiting example, a sample can be run through a chromatography column to isolate vesicles based on a property such as size of electrophoretic motility, and the vesicles can then be passed through a microfluidic device. Binding agents can be used before, during or after these steps.

Cell-of-Origin and Disease-Specific Vesicles

The bindings agent disclosed herein can be used to isolate or detect a vesicle, such as a cell-of-origin vesicle or vesicle with a specific biosignature. The binding agent can be used to isolate or detect a heterogeneous population of vesicles from a sample or can be used to isolate or detect a homogeneous population of vesicles, such as cell-of-origin specific vesicles with specific biosignatures, from a heterogeneous population of vesicles.

A homogeneous population of vesicles, such as cell-of-origin specific vesicles, can be analyzed and used to characterize a phenotype for a subject. Cell-of-origin specific vesicles are esicles derived from specific cell types, which can include, but are not limited to, cells of a specific tissue, cells from a specific tumor of interest or a diseased tissue of interest, circulating tumor cells, or cells of maternal or fetal origin. The vesicles may be derived from tumor cells or lung, pancreas, stomach, intestine, bladder, kidney, ovary, testis, skin, colorectal, breast, prostate, brain, esophagus, liver, placenta, or fetal cells. The isolated vesicle can also be from a particular sample type, such as urinary vesicle.

A cell-of-origin specific vesicle from a biological sample can be isolated using one or more binding agents that are specific to a cell-of-origin. Vesicles for analysis of a disease or condition can be isolated using one or more binding agent specific for biomarkers for that disease or condition.

A vesicle can be concentrated prior to isolation or detection of a cell-of-origin specific vesicle, such as through centrifugation, chromatography, or filtration, as described above, to produce a heterogeneous population of vesicles prior to isolation of cell-of-origin specific vesicles. Alternatively, the vesicle is not concentrated, or the biological sample is not enriched for a vesicle, prior to isolation of a cell-of-origin vesicle.

FIG. 61B illustrates a flowchart which depicts one method 6100B for isolating or identifying a cell-of-origin specific vesicle. First, a biological sample is obtained from a subject in step 6102. The sample can be obtained from a third party or from the same party performing the analysis. Next, cell-of-origin specific vesicles are isolated from the biological sample in step 6104. The isolated cell-of-origin specific vesicles are then analyzed in step 6106 and a biomarker or biosignature for a particular phenotype is identified in step 6108. The method may be used for a number of phenotypes. In some embodiments, prior to step 6104, vesicles are concentrated or isolated from a biological sample to produce a homogeneous population of vesicles. For example, a heterogeneous population of vesicles may be isolated using centrifugation, chromatography, filtration, or other methods as described above, prior to use of one or more binding agents specific for isolating or identifying vesicles derived from specific cell types.

A cell-of-origin specific vesicle can be isolated from a biological sample of a subject by employing one or more binding agents that bind with high specificity to the cell-of-origin specific vesicle. In some instances, a single binding agent can be employed to isolate a cell-of-origin specific vesicle. In other instances, a combination of binding agents may be employed to isolate a cell-of-origin specific vesicle. For example, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75, or 100 different binding agents may be used to isolate a cell-of-origin vesicle. Therefore, a vesicle population (e.g., vesicles having the same binding agent profile) can be identified by utilizing a single or a plurality of binding agents.

One or more binding agents can be selected based on their specificity for a target antigen(s) that is specific to a cell-of-origin, e.g., a cell-of-origin that is related to a tumor, autoimmune disease, cardiovascular disease, neurological disease, infection or other disease or disorder. The cell-of-origin can be from a cell that is informative for a diagnosis, prognosis, disease stratification, theranosis, prediction of responder/non-responder status, disease monitoring, treatment monitoring and the like as related to such diseases and disorders. The cell-of-origin can also be from a cell useful to discover biomarkers for use thereto. Non-limiting examples of antigens which may be used singularly, or in combination, to isolate a cell-of-origin specific vesicle, disease specific vesicle, or tumor specific vesicle, are shown in FIG. 1 and are also described herein. The antigen can comprise membrane bound antigens which are accessible to binding agents. The antigen can be a biomarker related to characterizing a phenotype.

One of skill will appreciate that any applicable antigen that can be used to isolate an informative vesicle is contemplated by the invention. Binding agents, e.g., antibodies, aptamers and lectins, can be chosen that recognize surface antigens and/or fragments thereof, as outlined herein. The binding agents can recognize antigens specific to the desired cell type or location and/or recognize biomarkers associated with the desired cells. The cells can be, e.g., tumor cells, other diseased cells, cells that serve as markers of disease such as activated immune cells, etc. One of skill will appreciate that binding agents for any cells of interest can be useful for isolating vesicles associated with those cells. One of skill will further appreciate that the binding agents disclosed herein can be used for detecting vesicles of interest. As a non-limiting example, a binding agent to a vesicle biomarker can be labeled directly or indirectly in order to detect vesicles bound by one of more of the same or different binding agents.

A number of targets for binding agents useful for binding to vesicles associated with cancer, autoimmune diseases, cardiovascular diseases, neurological diseases, infection or other disease or disorders are presented in Table 4. A vesicle derived from a cell associated with one of the listed disorders can be characterized using one of the antigens in the table. The binding agent, e.g., an antibody or aptamer, can recognize an epitope of the listed antigens, a fragment thereof, or binding agents can be used against any appropriate combination. Other antigens associated with the disease or disorder can be recognized as well in order to characterize the vesicle. One of skill will appreciate that any applicable antigen that can be used to assess an informative vesicle is contemplated by the invention for isolation, capture or detection in order to characterize a vesicle.

TABLE 4 Illustrative Antigens for Use in Characterizing Various Diseases and Disorders Disease or disorder Target Breast cancer, e.g., glandular or stromal cells BCA-225, hsp70, MART1, ER, VEGFA, Class III b- tubulin, HER2/neu (for Her2+ breast cancer), GPR30, ErbB4 (JM) isoform, MPR8, MISIIR Breast cancer CD9, MIS Rii, ER, CD63, MUC1, HER3, STAT3, VEGFA, BCA, CA125, CD24, EPCAM, ERB B4 Breast cancer BCA-225, hsp70, MART1, ER, VEGFA, Class III b- tubulin, HER2/neu (e.g., for Her2+ breast cancer), GPR30, ErbB4 (JM) isoform, MPR8, MISIIR, CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK- 1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, a progesterone receptor (PR) or its isoform (PR(A) or PR(B)), P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNF Breast cancer CD10, NPGP/NPFF2, HER2/ERBB2, AGTR1, NPY1R, neurokinin receptor-1 (NK-1 or NK-1R), NK- 2, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1 Breast cancer SPB, SPC, NSE, PGP9.5, CD9, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, EGFR, B7H3, IC3b, MUC1, mesothelin, SPA, PCSA, CD63, STEAP, AQP5, CD81, DR3, PSM, GPCR, EphA2, hCEA- CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, A33, CD24, CD10, NGAL, EpCam, MUC17, TROP-2, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR Breast cancer BRCA, MUC-1, MUC 16, CD24, ErbB4, ErbB2 (HER2), ErbB3, HSP70, Mammaglobin, PR, PR(B), VEGFA Ovarian Cancer CA125, VEGFR2, HER2, MISIIR, VEGFA, CD24 Lung Cancer CYFRA21-1, TPA-M, TPS, CEA, SCC-Ag, XAGE- 1b, HLA Class 1, TA-MUC1, KRAS, hENT1, kinin B1 receptor, kinin B2 receptor, TSC403, HTI56, DC- LAMP Lung Cancer SPB, SPC, PSP9.5, NDUFB7, gal3-b2c10, iC3b, MUC1, GPCR, CABYR and muc17 Colorectal Cancer CEA, MUC2, GPA33, CEACAM5, ENFB1, CCSA-3, CCSA-4, ADAM10, CD44, NG2, ephrin B1, plakoglobin, galectin 4, RACK1, tetraspanin-8, FASL, A33, CEA, EGFR, dipeptidase 1, PTEN, Na(+)- dependent glucose transporter, UDP- glucuronosyltransferase 1A, TMEM211, CD24 Prostate Cancer PSA, TMPRSS2, FASLG, TNFSF10, PSMA, NGEP, I1-7RI, CSCR4, CysLT1R, TRPM8, Kv1.3, TRPV6, TRPM8, PSGR, MISIIR, galectin-3, PCA3, TMPRSS2:ERG Brain Cancer PRMT8, BDNF, EGFR, DPPX, Elk, Densin-180, BAI2, BAI3 Blood Cancer (hematological malignancy) CD44, CD58, CD31, CD11a, CD49d, GARP, BTS, Raftlin Melanoma DUSP1, TYRP1, SILV, MLANA, MCAM, CD63, Alix, hsp70, meosin, p120 catenin, PGRL, syntaxin binding protein 1 & 2, caveolin Liver Cancer (hepatocellular carcinoma) HBxAg, HBsAg, NLT Cervical Cancer MCT-1, MCT-2, MCT-4 Endometrial Cancer Alpha V Beta 6 integrin Psoriasis flt-1, VPF receptors, kdr Autoimmune Disease Tim-2 Irritable Bowel Disease (IBD or Syndrome (IBS) IL-16, IL-1beta, IL-12, TNF-alpha, interferon-gamma, IL-6, Rantes, II-12, MCP-1, 5HT Diabetes, e.g., pancreatic cells IL-6, CRP, RBP4 Barrett's Esophagus p53, MUC1, MUC6 Fibromyalgia neopterin, gp130 Benign Prostatic Hyperplasia (BPH) KIA1, intact fibronectin Multiple Sclerosis B7, B7-2, CD-95 (fas), Apo-1/Fas Parkinson's Disease PARK2, ceruloplasmin, VDBP, tau, DJ-1 Rheumatic Disease Citrulinated fibrin a-chain, CD5 antigen-like fibrinogen fragment D, CD5 antigen-like fibrinogen fragment B, TNF alpha Alzheimer's Disease APP695, APP751 or APP770, BACE1, cystatin C, amyloid β, T-tau, complement factor H, alpha-2- macroglobulin Head and Neck Cancer EGFR, EphB4, Ephrin B2 Gastrointestinal Stromal Tumor (GIST) c-kit PDGFRA, NHE-3 Renal Cell Carcinoma c PDGFRA, VEGF, HIF 1 alpha Schizophrenia ATP5B, ATP5H, ATP6V1B, DNM1 Peripheral Neuropathic Pain OX42, ED9 Chronic Neuropathic Pain chemokine receptor (CCR2/4) Prion Disease PrPSc, 14-3-3 zeta, S-100, AQP4 Stroke S-100, neuron specific enolase, PARK7, NDKA, ApoC-I, ApoC-III, SAA or AT-III fragment, Lp- PLA2, hs-CRP Cardiovascular Disease FATP6 Esophageal Cancer CaSR Tuberculosis antigen 60, HSP, Lipoarabinomannan, Sulfolipid, antigen of acylated trehalose family, DAT, TAT, Trehalose 6,6-dimycolate (cord-factor) antigen HIV gp41, gp120 Autism VIP, PACAP, CGRP, NT3 Asthma YKL-40, S-nitrosothiols, SSCA2, PAI, amphiregulin, periostin Lupus TNFR Cirrhosis NLT, HBsAg Influenza hemagglutinin, neurominidase Vulnerable Plaque Alpha v. Beta 3 integrin, MMP9

A cell-of-origin specific vesicle may be isolated using novel binding agents, using methods as described herein. Furthermore, a cell-of-origin specific vesicle can also be isolated from a biological sample using isolation methods based on cellular binding partners or binding agents of such vesicles. Such cellular binding partners can include but are not limited to peptides, proteins, RNA, DNA, apatmers, cells or serum-associated proteins that only bind to such vesicles when one or more specific biomarkers are present. Isolation or detection of a cell-of-origin specific vesicle can be carried out with a single binding partner or binding agent, or a combination of binding partners or binding agents whose singular application or combined application results in cell-of-origin specific isolation or detection. Non-limiting examples of such binding agents are provided in FIG. 2. For example, a vesicle for characterizing breast cancer can be isolated with one or more binding agents including, but not limited to, estrogen, progesterone, trastuzumab, CCND1, MYC PNA, IGF-1 PNA, MYC PNA, SC4 aptamer (Ku), All-7 aptamer (ERB2), Galectin-3, mucin-type O-glycans, L-PHA, Galectin-9, or any combination thereof.

A binding agent may also be used for isolating or detecting a cell-of-origin specific vesicle based on: i) the presence of antigens specific for cell-of-origin specific vesicles; ii) the absence of markers specific for cell-of-origin specific vesicles; or iii) expression levels of biomarkers specific for cell-of-origin specific vesicles. A heterogeneous population of vesicles can be applied to a surface coated with specific binding agents designed to rule out or identify the cell-of-origin characteristics of the vesicles. Various binding agents, such as antibodies, can be arrayed on a solid surface or substrate and the heterogeneous population of vesicles is allowed to contact the solid surface or substrate for a sufficient time to allow interactions to take place. Specific binding or non-binding to given antibody locations on the array surface or substrate can then serve to identify antigen specific characteristics of the vesicle population that are specific to a given cell-of-origin. That is, binding events can signal the presence of a vesicle having an antigen recognized by the bound antibody. Conversely, lack of binding events can signal the absence of vesicles having an antigen recognized by the bound antibody.

A cell-of-origin specific vesicle can be enriched or isolated using one or more binding agents using a magnetic capture method, fluorescence activated cell sorting (FACS) or laser cytometry as described above. Magnetic capture methods can include, but are not limited to, the use of magnetically activated cell sorter (MACS) microbeads or magnetic columns. Examples of immunoaffinity and magnetic particle methods that can be used are described in U.S. Pat. Nos. 4,551,435, 4,795,698, 4,925,788, 5,108,933, 5,186,827, 5,200,084 or 5,158,871. A cell-of-origin specific vesicle can also be isolated following the general methods described in U.S. Pat. No. 7,399,632, by using combination of antigens specific to a vesicle.

Any other appropriate method for isolating or otherwise enriching the cell-of-origin specific vesicles with respect to a biological sample may also be used according to the present invention. For example, size exclusion chromatography such as gel permeation columns, centrifugation or density gradient centrifugation, and filtration methods can be used in combination with the antigen selection methods described herein. The cell-of-origin specific vesicles may also be isolated following the methods described in Koga et al., Anticancer Research, 25:3703-3708 (2005), Taylor et al., Gynecologic Oncology, 110:13-21 (2008), Nanjee et al., Clin Chem, 2000; 46:207-223 or U.S. Pat. No. 7,232,653.

Vesicles can be isolated and/or detected to provide diagnosis, prognosis, disease stratification, theranosis, prediction of responder/non-responder status, disease monitoring, treatment monitoring and the like. In one embodiment, vesicles are isolated from cells having a disease or disorder, e.g., cells derived from a malignant cell, a site of autoimmune disease, cardiovascular disease, neurological disease, or infection. In some embodiments, the isolated vesicles are derived from cells related to such diseases and disorders, e.g., immune cells that play a role in the etiology of the disease and whose analysis is informative for a diagnosis, prognosis, disease stratification, theranosis, prediction of responder/non-responder status, disease monitoring, treatment monitoring and the like as relates to such diseases and disorders. The vesicles are further useful to discover novel biomarkers. By identifying biomarkers associated with vesicles, isolated vesicles can be assessed for characterizing a phenotype as described herein.

Biomarker Assessment

In an aspect of the invention, a phenotype of a subject is characterized by analyzing a biological sample and determining the presence, level, amount, or concentration of one or more populations of circulating biomarkers in the sample, e.g., circulating vesicles, proteins or nucleic acids. In embodiments, characterization includes determining whether the circulating biomarkers in the sample are altered as compared to a reference, which can also be referred to a standard or a control. An alteration can include any measurable difference between the sample and the reference, including without limitation an absolute presence or absence, a quantitative level, a relative level compared to a reference, e.g., the level of all vesicles present, the level of a housekeeping marker, and/or the level of a spiked-in marker, an elevated level, a decreased level, overexpression, underexpression, differential expression, a mutation or other altered sequence, a modification (glycosylation, phosphorylation, epigenetic change) and the like. In some embodiments, circulating biomarkers are purified or concentrated from a sample prior to determining their amount. Unless otherwise specified, “purified” or “isolated” as used herein refer to partial or complete purification or isolation. In other embodiments, circulating biomarkers are directly assessed from a sample, without prior purification or concentration. Circulating vesicles can be cell-of-origin specific vesicles or vesicles with a specific biosignature. A biosignature includes specific pattern of biomarkers, e.g., patterns of biomarkers indicative of a phenotype that is desirable to detect, such as a disease phenotype. The biosignature can comprise one or more circulating biomarkers. A biosignature can be used when characterizing a phenotype, such as a diagnosis, prognosis, theranosis, or prediction of responder/non-responder status. In some embodiments, the biosignature is used to determine a physiological or biological state, such as pregnancy or the stage of pregnancy. The biosignature can also be used to determine treatment efficacy, stage of a disease or condition, or progression of a disease or condition. For example, the amount of one or more vesicles can be proportional or inversely proportional to an increase in disease stage or progression. The detected amount of vesicles can also be used to monitor progression of a disease or condition or to monitor a subject's response to a treatment.

The circulating biomarkers can be evaluated by comparing the level of circulating biomarkers with a reference level or value. The reference value can be particular to physical or temporal endpoint. For example, the reference value can be from the same subject from whom a sample is assessed, or the reference value can be from a representative population of samples (e.g., samples from normal subjects not exhibiting a symptom of disease). Therefore, a reference value can provide a threshold measurement which is compared to a subject sample's readout for a biosignature assayed in a given sample. Such reference values may be set according to data pooled from groups of sample corresponding to a particular cohort, including but not limited to age (e.g., newborns, infants, adolescents, young, middle-aged adults, seniors and adults of varied ages), racial/ethnic groups, normal versus diseased subjects, smoker v. non-smoker, subject receiving therapy versus untreated subject, different time points of treatment for a particular individual or group of subjects similarly diagnosed or treated or combinations thereof. Furthermore, by determining a biosignature at different timepoints of treatment for a particular individual, the individual's response to the treatment or progression of a disease or condition for which the individual is being treated for, can be monitored.

A reference value may be based on samples assessed from the same subject so to provide individualized tracking. In some embodiments, frequent testing of a biosignature in samples from a subject provides better comparisons to the reference values previously established for that subject. Such time course measurements are used to allow a physician to more accurately assess the subject's disease stage or progression and therefore inform a better decision for treatment. In some cases, the variance of a biosignature is reduced when comparing a subject's own biosignature over time, thus allowing an individualized threshold to be defined for the subject, e.g., a threshold at which a diagnosis is made. Temporal intrasubject variation allows each individual to serve as their own longitudinal control for optimum analysis of disease or physiological state. As an illustrative example, consider that the level of vesicles derived from prostate cells is measured in a subject's blood over time. A spike in the level of prostate-derived vesicles in the subject's blood can indicate hyperproliferation of prostate cells, e.g., due to prostate cancer.

Reference values can be established for unaffected individuals (of varying ages, ethnic backgrounds and sexes) without a particular phenotype by determining the biosignature of interest in an unaffected individual. For example, a reference value for a reference population can be used as a baseline for detection of one or more circulating biomarker populations in a test subject. If a sample from a subject has a level or value that is similar to the reference, the subject can be identified to not have the disease, or of having a low likelihood of developing a disease.

Alternatively, reference values or levels can be established for individuals with a particular phenotype by determining the amount of one or more populations of vesicles in an individual with the phenotype. In addition, an index of values can be generated for a particular phenotype. For example, different disease stages can have different values, such as obtained from individuals with the different disease stages. A subject's value can be compared to the index and a diagnosis or prognosis of the disease can be determined, such as the disease stage or progression wherein the subject's levels most closely correlate with the index. In other embodiments, an index of values is generated for therapeutic efficacies. For example, the level of vesicles of individuals with a particular disease can be generated and noted what treatments were effective for the individual. The levels can be used to generate values of which is a subject's value is compared, and a treatment or therapy can be selected for the individual, e.g., by predicting from the levels whether the subject is likely to be a responder or non-responder for a treatment.

In some embodiments, a reference value is determined for individuals unaffected with a particular cancer, by isolating or detecting circulating biomarkers with an antigen that specifically targets biomarkers for the particular cancer. As a non-limiting example, individuals with varying stages of colorectal cancer and noncancerous polyps can be surveyed using the same techniques described for unaffected individuals and the levels of circulating vesicles for each group can be determined. In some embodiments, the levels are defined as means±standard deviations from at least two separate experiments, performed in at least duplicate or triplicate. Comparisons between these groups can be made using statistical tests to determine statistical significance of distinguishing biomarkers observed. In some embodiments, statistical significance is determined using a parametric statistical test. The parametric statistical test can comprise, without limitation, a fractional factorial design, analysis of variance (ANOVA), a t-test, least squares, a Pearson correlation, simple linear regression, nonlinear regression, multiple linear regression, or multiple nonlinear regression. Alternatively, the parametric statistical test can comprise a one-way analysis of variance, two-way analysis of variance, or repeated measures analysis of variance. In other embodiments, statistical significance is determined using a nonparametric statistical test. Examples include, but are not limited to, a Wilcoxon signed-rank test, a Mann-Whitney test, a Kruskal-Wallis test, a Friedman test, a Spearman ranked order correlation coefficient, a Kendall Tau analysis, and a nonparametric regression test. In some embodiments, statistical significance is determined at a p-value of less than 0.05, 0.01, 0.005, 0.001, 0.0005, or 0.0001. The p-values can also be corrected for multiple comparisons, e.g., using a Bonferroni correction, a modification thereof, or other technique known to those in the art, e.g., the Hochberg correction, Holm-Bonferroni correction, {hacek over (S)}idák correction, Dunnett's correction or Tukey's multiple comparisons. In some embodiments, an ANOVA is followed by Tukey's correction for post-test comparing of the biomarkers from each population.

Reference values can also be established for disease recurrence monitoring (or exacerbation phase in MS), for therapeutic response monitoring, or for predicting responder/non-responder status.

In some embodiments, a reference value for vesicles is determined using an artificial vesicle, also referred to herein as a synthetic vesicle. Methods for manufacturing artificial vesicles are known to those of skill in the art, e.g., using liposomes. Artificial vesicles can be manufactured using methods disclosed in US20060222654 and U.S. Pat. No. 4,448,765, which are incorporated herein by reference in its entirety. Artificial vesicles can be constructed with known markers to facilitate capture and/or detection. In some embodiments, artificial vesicles are spiked into a bodily sample prior to processing. The level of intact synthetic vesicle can be tracked during processing, e.g., using filtration or other isolation methods disclosed herein, to provide a control for the amount of vesicles in the initial versus processed sample. Similarly, artificial vesicles can be spiked into a sample before or after any processing steps. In some embodiments, artificial vesicles are used to calibrate equipment used for isolation and detection of vesicles.

Artificial vesicles can be produced and used a control to test the viability of an assay, such as a bead-based assay. The artificial vesicle can bind to both the beads and to the detection antibodies. Thus, the artificial vesicle contains the amino acid sequence/conformation that each of the antibodies binds. The artificial vesicle can comprise a purified protein or a synthetic peptide sequence to which the antibody binds. The artificial vesicle could be a bead, e.g., a polystyrene bead, that is capable of having biological molecules attached thereto. If the bead has an available carboxyl group, then the protein or peptide could be attached to the bead via an available amine group, such as using carbodiimide coupling.

In another embodiment, the artificial vesicle can be a polystyrene bead coated with avidin and a biotin is placed on the protein or peptide of choice either at the time of synthesis or via a biotin-maleimide chemistry. The proteins/peptides to be on the bead can be mixed together in ratio specific to the application the artificial vesicle is being used for, and then conjugated to the bead. These artificial vesicles can then serve as a link between the capture beads and the detection antibodies, thereby providing a control to show that the components of the assay are working properly.

The value can be a quantitative or qualitative value. The value can be a direct measurement of the level of vesicles (example, mass per volume), or an indirect measure, such as the amount of a specific biomarker. The value can be a quantitative, such as a numerical value. In other embodiments, the value is qualitiative, such as no vesicles, low level of vesicles, medium level, high level of vesicles, or variations thereof.

The reference value can be stored in a database and used as a reference for the diagnosis, prognosis, theranosis, disease stratification, disease monitoring, treatment monitoring or prediction of non-responder/responder status of a disease or condition based on the level or amount of circulation biomarkers, such as total amount of vesicles or microRNA, or the amount of a specific population of vesicles or microRNA, such as cell-of-origin specific vesicles or microRNA or microRNA from vesicles with a specific biosignature. In an illustrative example, consider a method of determining a diagnosis for a cancer. Vesicles or other circulation biomarkers from reference subjects with and without the cancer are assessed and stored in the database. The reference subjects provide biosignature indicative of the cancer or of another state, e.g., a healthy state. A sample from a test subject is then assayed and the microRNA biosignature is compared against those in the database. If the subject's biosignature correlates more closely with reference values indicative of cancer, a diagnosis of cancer may be made. Conversely, if the subject's biosignature correlates more closely with reference values indicative of a healthy state, the subject may be determined to not have the disease. One of skill will appreciate that this example is non-limiting and can be expanded for assessing other phenotypes, e.g., other diseases, prognosis, theranosis, disease stratification, disease monitoring, treatment monitoring or prediction of non-responder/responder status, and the like.

A biosignature for characterizing a phenotype can be determined by detecting circulating biomarkers such as vesicles, including biomarkers associate with vesicles such as surface antigens or payload. The payload, e.g., protein or species of RNA such as mRNA or microRNA, can be assessed within a vesicle. Alternately, the payload in a sample is analyzed to characterize the phenotype without isolating the payload from the vesicles. Many analytical techniques are available to assess vesicles. In some embodiments, vesicle levels are characterized using mass spectrometry, flow cytometry, immunocytochemical staining, Western blotting, electrophoresis, chromatography or x-ray crystallography in accordance with procedures known in the art. For example, vesicles can be characterized and quantitatively measured using flow cytometry as described in Clayton et al., Journal of Immunological Methods 2001; 163-174, which is herein incorporated by reference in its entirety. Vesicle levels may be determined using binding agents as described above. For example, a binding agent to vesicles can be labeled and the label detected and used to determine the amount of vesicles in a sample. The binding agent can be bound to a substrate, such as arrays or particles, such as described above. Alternatively, the vesicles may be labeled directly.

Electrophoretic tags or eTags can be used to determine the amount of vesicles. eTags are small fluorescent molecules linked to nucleic acids or antibodies and are designed to bind one specific nucleic acid sequence or protein, respectively. After the eTag binds its target, an enzyme is used to cleave the bound eTag from the target. The signal generated from the released eTag, called a “reporter,” is proportional to the amount of target nucleic acid or protein in the sample. The eTag reporters can be identified by capillary electrophoresis. The unique charge-to-mass ratio of each eTag reporter—that is, its electrical charge divided by its molecular weight—makes it show up as a specific peak on the capillary electrophoresis readout Thus by targeting a specific biomarker of a vesicle with an eTag, the amount or level of vesicles can be determined

The vesicle level can determined from a heterogeneous population of vesicles, such as the total population of vesicles in a sample. Alternatively, the vesicles level is determined from a homogenous population, or substantially homogenous population of vesicles, such as the level of specific cell-of-origin vesicles, such as vesicles from prostate cancer cells. In yet other embodiments, the level is determined for vesicles with a particular biomarker or combination of biomarkers, such as a biomarker specific for prostate cancer. Determining the level vesicles can be performed in conjunction with determining the biomarker or combination of biomarkers of a vesicle. Alternatively, determining the amount of vesicle may be performed prior to or subsequent to determining the biomarker or combination of biomarkers of the vesicles.

Determining the amount of vesicles can be assayed in a multiplexed manner. For example, determining the amount of more than one population of vesicles, such as different cell-of-origin specific vesicles with different biomarkers or combination of biomarkers, can be performed, such as those disclosed herein.

Performance of a diagnostic or related test is typically assessed using statistical measures. The performance of the characterization can be assessed by measuring sensitivity, specificity and related measures. For example, a level of circulation biomarkers of interest can be assayed to characterize a phenotype, such as detecting a disease. The sensitivity and specificity of the assay to detect the disease is determined

A true positive is a subject with a characteristic, e.g., a disease or disorder, correctly identified as having the characteristic. A false positive is a subject without the characteristic that the test improperly identifies as having the characteristic. A true negative is a subject without the characteristic that the test correctly identifies as not having the characteristic. A false negative is a person with the characteristic that the test improperly identifies as not having the characteristic. The ability of the test to distinguish between these classes provides a measure of test performance.

The specificity of a test is defined as the number of true negatives divided by the number of actual negatives (i.e., sum of true negatives and false positives). Specificity is a measure of how many subjects are correctly identified as negatives. A specificity of 100% means that the test recognizes all actual negatives—for example, all healthy people will be recognized as healthy. A lower specificity indicates that more negatives will be determined as positive.

The sensitivity of a test is defined as the number of true positives divided by the number of actual positives (i.e., sum of true positives and false negatives). Specificity is a measure of how many subjects are correctly identified as positives. A sensitivity of 100% means that the test recognizes all actual positives—for example, all sick people will be recognized as sick. A lower sensitivity indicates that more positives will be missed by being determined as negative.

The accuracy of a test is defined as the number of true positives and true negatives divided by the sum of all true and false positives and all true and false negatives. It provides one number that combines sensitivity and specificity measurements.

Sensitivity, specificity and accuracy are determined at a particular discrimination threshold value. For example, a common threshold for prostate cancer (PCa) detection is 4 ng/mL of prostate specific antigen (PSA) in serum. A level of PSA equal to or above the threshold is considered positive for PCa and any level below is considered negative. As the threshold is varied, the sensitivity and specificity will also vary. For example, as the threshold for detecting cancer is increased, the specificity will increase because it is harder to call a subject positive, resulting in fewer false positives. At the same time, the sensitivity will decrease. A receiver operating characteristic curve (ROC curve) is a graphical plot of the true positive rate (i.e., sensitivity) versus the false positive rate (i.e., 1—specificity) for a binary classifier system as its discrimination threshold is varied. The ROC curve shows how sensitivity and specificity change as the threshold is varied. The Area Under the Curve (AUC) of an ROC curve provides a summary value indicative of a test's performance over the entire range of thresholds. The AUC is equal to the probability that a classifier will rank a randomly chosen positive sample higher than a randomly chosen negative sample. An AUC of 0.5 indicates that the test has a 50% chance of proper ranking, which is equivalent to no discriminatory power (a coin flip also has a 50% chance of proper ranking). An AUC of 1.0 means that the test properly ranks (classifies) all subjects. The AUC is equivalent to the Wilcoxon test of ranks.

A biosignature according to the invention can be used to characterize a phenotype with at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, or 70% sensitivity, such as with at least 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, or 87% sensitivity. In some embodiments, the phenotype is characterized with at least 87.1, 87.2, 87.3, 87.4, 87.5, 87.6, 87.7, 87.8, 87.9, 88.0, or 89% sensitivity, such as at least 90% sensitivity. The phenotype can be characterized with at least 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% sensitivity.

A biosignature according to the invention can be used to characterize a phenotype of a subject with at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97% specificity, such as with at least 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100% specificity.

A biosignature according to the invention can be used to characterize a phenotype of a subject, e.g., based on a level of a circulating biomarker or other characteristic, with at least 50% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 55% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 60% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 65% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 70% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 75% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 80% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 85% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 86% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 87% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 88% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 89% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 90% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 91% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 92% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 93% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 94% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 95% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 96% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 97% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 98% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; at least 99% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity; or substantially 100% sensitivity and at least 60, 65, 70, 75, 80, 85, 90, 95, 99, or 100% specificity.

A biosignature according to the invention can be used to characterize a phenotype of a subject with at least 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97% accuracy, such as with at least 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100% accuracy.

In some embodiments, a biosignature according to the invention is used to characterize a phenotype of a subject with an AUC of at least 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, or 0.97, such as with at least 0.971, 0.972, 0.973, 0.974, 0.975, 0.976, 0.977, 0.978, 0.978, 0.979, 0.980, 0.981, 0.982, 0.983, 0.984, 0.985, 0.986, 0.987, 0.988, 0.989, 0.99, 0.991, 0.992, 0.993, 0.994, 0.995, 0.996, 0.997, 0.998, 0.999 or 1.00.

Furthermore, the confidence level for determining the specificity, sensitivity, accuracy or AUC, may be determined with at least 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% confidence.

Other related performance measures include positive and negative likelihood ratios [positive LR=sensitivity/(1−specificity); negative LR=(1−sensitivity)/specificity]. Such measures can also be used to gauge test performance according to the methods of the invention.

Classification

Biosignature according to the invention can be used to classify a sample. Techniques for discriminate analysis are known to those of skill in the art. For example, a sample can be classified as, or predicted to be, a responder or non-responder to a given treatment for a given disease or disorder. Many statistical classification techniques are known to those of skill in the art. In supervised learning approaches, a group of samples from two or more groups are analyzed with a statistical classification method. Biomarkers can be discovered that can be used to build a classifier that differentiates between the two or more groups. A new sample can then be analyzed so that the classifier can associate the new with one of the two or more groups. Commonly used supervised classifiers include without limitation the neural network (multi-layer perceptron), support vector machines, k-nearest neighbors, Gaussian mixture model, Gaussian, naive Bayes, decision tree and radial basis function (RBF) classifiers. Linear classification methods include Fisher's linear discriminant, logistic regression, naive Bayes classifier, perceptron, and support vector machines (SVMs). Other classifiers for use with the invention include quadratic classifiers, k-nearest neighbor, boosting, decision trees, random forests, neural networks, pattern recognition, Bayesian networks and Hidden Markov models. One of skill will appreciate that these or other classifiers, including improvements of any of these, are contemplated within the scope of the invention.

Classification using supervised methods is generally performed by the following methodology:

In order to solve a given problem of supervised learning (e.g. learning to recognize handwriting) one has to consider various steps:

1. Gather a training set. These can include, for example, samples that are from a subject with or without a disease or disorder, subjects that are known to respond or not respond to a treatment, subjects whose disease progresses or does not progress, etc. The training samples are used to “train” the classifier.

2. Determine the input “feature” representation of the learned function. The accuracy of the learned function depends on how the input object is represented. Typically, the input object is transformed into a feature vector, which contains a number of features that are descriptive of the object. The number of features should not be too large, because of the curse of dimensionality; but should be large enough to accurately predict the output. The features might include a set of biomarkers such as those derived from vesicles as described herein.

3. Determine the structure of the learned function and corresponding learning algorithm. A learning algorithm is chosen, e.g., artificial neural networks, decision trees, Bayes classifiers or support vector machines. The learning algorithm is used to build the classifier.

4. Build the classifier. The learning algorithm is run the gathered training set. Parameters of the learning algorithm may be adjusted by optimizing performance on a subset (called a validation set) of the training set, or via cross-validation. After parameter adjustment and learning, the performance of the algorithm may be measured on a test set of naive samples that is separate from the training set.

Once the classifier is determined as described above, it can be used to classify a sample, e.g., that of a subject who is being analyzed by the methods of the invention. As an example, a classifier can be built using data for levels of circulation biomarkers of interest in reference subjects with and without a disease as the training and test sets. Circulating biomarker levels found in a sample from a test subject are assessed and the classifier is used to classify the subject as with or without the disease. As another example, a classifier can be built using data for levels of vesicle biomarkers of interest in reference subjects that have been found to respond or not respond to certain diseases as the training and test sets. The vesicle biomarker levels found in a sample from a test subject are assessed and the classifier is used to classify the subject as with or without the disease.

Unsupervised learning approaches can also be used with the invention. Clustering is an unsupervised learning approach wherein a clustering algorithm correlates a series of samples without the use the labels. The most similar samples are sorted into “clusters.” A new sample could be sorted into a cluster and thereby classified with other members that it most closely associates. Many clustering algorithms well known to those of skill in the art can be used with the invention, such as hierarchical clustering.

Biosignatures

A biosignature can be obtained according to the invention by assessing a vesicle population, including surface and payload vesicle associated biomarkers, and/or circulating biomarkers including microRNA and protein. A biosignature derived from a subject can be used to characterize a phenotype of the subject. A biosignature can further include the level of one or more additional biomarkers, e.g., circulating biomarkers or biomarkers associated with a vesicle of interest. A biosignature of a vesicle of interest can include particular antigens or biomarkers that are present on the vesicle. The biosignature can also include one or more antigens or biomarkers that are carried as payload within the vesicle, including the microRNA under examination. The biosignature can comprise a combination of one or more antigens or biomarkers that are present on the vesicle with one or more biomarkers that are detected in the vesicle. The biosignature can further comprise other information about a vesicle aside from its biomarkers. Such information can include vesicle size, circulating half-life, metabolic half-life, and specific activity in vivo or in vitro. The biosignature can comprise the biomarkers or other characteristics used to build a classifier.

To assay in the context of additional biomarkers means that the sample, whether isolated cMVs, biological fluid, or other sample, is placed in contact with additional biomarkers that may or may not bind their specific target biomarker to provide a biosignature for the sample.

In some embodiments, the microRNA is detected directly in a biological sample. For example, RNA in a bodily fluid can be isolated using commercially available kits such as mirVana kits (Applied Biosystems/Ambion, Austin, Tex.), MagMAX™ RNA Isolation Kit (Applied Biosystems/Ambion, Austin, Tex.), and QIAzol Lysis Reagent and RNeasy Midi Kit (Qiagen Inc., Valencia Calif.). Particular species of microRNAs can be determined using array or PCR techniques as described below.

In some embodiments, the microRNA payload with vesicles is assessed in order to characterize a phenotype. The vesicles can be purified or concentrated prior to determining the biosignature. For example, a cell-of-origin specific vesicle can be isolated and its biosignature determined. Alternatively, the biosignature of the vesicle can be directly assayed from a sample, without prior purification or concentration. The biosignature of the invention can be used to determine a diagnosis, prognosis, or theranosis of a disease or condition or similar measures described herein. A biosignature can also be used to determine treatment efficacy, stage of a disease or condition, or progression of a disease or condition, or responder/non-responder status. Furthermore, a biosignature may be used to determine a physiological state, such as pregnancy.

A characteristic of a vesicle in and of itself can be assessed to determine a biosignature. The characteristic can be used to diagnose, detect or determine a disease stage or progression, the therapeutic implications of a disease or condition, or characterize a physiological state. Such characteristics include without limitation the level or amount of vesicles, vesicle size, temporal evaluation of the variation in vesicle half-life, circulating vesicle half-life, metabolic half-life of a vesicle, or activity of a vesicle.

Biomarkers that can be included in a biosignature include one or more proteins or peptides (e.g., providing a protein signature), nucleic acids (e.g. RNA signature as described, or a DNA signature), lipids (e.g. lipid signature), or combinations thereof. In some embodiments, the biosignature can also comprise the type or amount of drug or drug metabolite present in a vesicle, (e.g., providing a drug signature), as such drug may be taken by a subject from which the biological sample is obtained, resulting in a vesicle carrying the drug or metabolites of the drug.

A biosignature can also include an expression level, presence, absence, mutation, variant, copy number variation, truncation, duplication, modification, or molecular association of one or more biomarkers. A genetic variant, or nucleotide variant, refers to changes or alterations to a gene or cDNA sequence at a particular locus, including, but not limited to, nucleotide base deletions, insertions, inversions, and substitutions in the coding and non-coding regions. Deletions may be of a single nucleotide base, a portion or a region of the nucleotide sequence of the gene, or of the entire gene sequence. Insertions may be of one or more nucleotide bases. The genetic variant may occur in transcriptional regulatory regions, untranslated regions of mRNA, exons, introns, or exon/intron junctions. The genetic variant may or may not result in stop codons, frame shifts, deletions of amino acids, altered gene transcript splice forms or altered amino acid sequence.

In an embodiment, nucleic acid biomarkers, including nucleic acid payload within a vesicle, is assessed for nucleotide variants. The nucleic acid biomarker may comprise one or more RNA species, e.g., mRNA, miRNA, snoRNA, snRNA, rRNAs, tRNAs, siRNA, hnRNA, shRNA, or a combination thereof. Similarly, DNA payload can be assessed to form a DNA signature.

An RNA signature or DNA signature can also include a mutational, epigenetic modification, or genetic variant analysis of the RNA or DNA present in the vesicle. Epigenetic modifications include patterns of DNA methylation. See, e.g., Lesche R. and Eckhardt F., DNA methylation markers: a versatile diagnostic tool for routine clinical use. Curr Opin Mol Ther. 2007 June; 9(3):222-30, which is incorporated herein by reference in its entirety. Thus, a biomarker can be the methylation status of a segment of DNA.

A biosignature can comprise one or more miRNA signatures combined with one or more additional signatures including, but not limited to, an mRNA signature, DNA signature, protein signature, peptide signature, antigen signature, or any combination thereof. For example, the biosignature can comprise one or more miRNA biomarkers with one or more DNA biomarkers, one or more mRNA biomarkers, one or more snoRNA biomarkers, one or more protein biomarkers, one or more peptide biomarkers, one or more antigen biomarkers, one or more antigen biomarkers, one or more lipid biomarkers, or any combination thereof.

A biosignature can comprise a combination of one or more antigens or binding agents (such as ability to bind one or more binding agents), such as listed in FIGS. 1 and 2, respectively, or those described elsewhere herein. The biosignature can further comprise one or more other biomarkers, such as, but not limited to, miRNA, DNA (e.g. single stranded DNA, complementary DNA, or noncoding DNA), or mRNA. The biosignature of a vesicle can comprise a combination of one or more antigens, such as shown in FIG. 1, one or more binding agents, such as shown in FIG. 2, and one or more biomarkers for a condition or disease, such as listed in FIGS. 3-60. The biosignature can comprise one or more biomarkers, for example miRNA, with one or more antigens specific for a cancer cell (for example, as shown in FIG. 1). The biosignature can also be derived from surface markers on the vesicle and/or payload markers from within the vesicle (e.g., miRNA payload).

In some embodiments, a vesicle used in the subject methods has a biosignature that is specific to the cell-of-origin and is used to derive disease-specific or biological state specific diagnostic, prognostic or therapy-related biosignatures representative of the cell-of-origin. In other embodiments, a vesicle has a biosignature that is specific to a given disease or physiological condition that is different from the biosignature of the cell-of-origin for use in the diagnosis, prognosis, staging, therapy-related determinations or physiological state characterization. Biosignatures can also comprise a combination of cell-of-origin specific and non-specific vesicles.

Biosignatures can be used to evaluate diagnostic criteria such as presence of disease, disease staging, disease monitoring, disease stratification, or surveillance for detection, metastasis or recurrence or progression of disease. A biosignature can also be used clinically in making decisions concerning treatment modalities including therapeutic intervention. A biosignature can further be used clinically to make treatment decisions, including whether to perform surgery or what treatment standards should be utilized along with surgery (e.g., either pre-surgery or post-surgery). As an illustrative example, a biosignature of circulating biomarkers that indicates an aggressive form of cancer may call for a more aggressive surgical procedure and/or more aggressive therapeutic regimen to treat the patient.

A biosignature can be used in therapy related diagnostics to provide tests useful to diagnose a disease or choose the correct treatment regimen, such as provide a theranosis. Theranostics includes diagnostic testing that provides the ability to affect therapy or treatment of a diseased state. Theranostics testing provides a theranosis in a similar manner that diagnostics or prognostic testing provides a diagnosis or prognosis, respectively. As used herein, theranostics encompasses any desired form of therapy related testing, including predictive medicine, personalized medicine, integrated medicine, pharmacodiagnostics and Dx/Rx partnering. Therapy related tests can be used to predict and assess drug response in individual subjects, i.e., to provide personalized medicine. Predicting a drug response can be determining whether a subject is a likely responder or a likely non-responder to a candidate therapeutic agent, e.g., before the subject has been exposed or otherwise treated with the treatment. Assessing a drug response can be monitoring a response to a drug, e.g., monitoring the subject's improvement or lack thereof over a time course after initiating the treatment. Therapy related tests are useful to select a subject for treatment who is particularly likely to benefit from the treatment or to provide an early and objective indication of treatment efficacy in an individual subject. Thus, a biosignature as disclosed herein may indicate that treatment should be altered to select a more promising treatment, thereby avoiding the great expense of delaying beneficial treatment and avoiding the financial and morbidity costs of administering an ineffective drug(s).

Therapy related diagnostics are also useful in clinical diagnosis and management of a variety of diseases and disorders, which include, but are not limited to cardiovascular disease, cancer, infectious diseases, sepsis, neurological diseases, central nervous system related diseases, endovascular related diseases, and autoimmune related diseases. Therapy related diagnostics also aid in the prediction of drug toxicity, drug resistance or drug response. Therapy related tests may be developed in any suitable diagnostic testing format, which include, but are not limited to, e.g., immunohistochemical tests, clinical chemistry, immunoassay, cell-based technologies, nucleic acid tests or body imaging methods. Therapy related tests can further include but are not limited to, testing that aids in the determination of therapy, testing that monitors for therapeutic toxicity, or response to therapy testing. Thus, a biosignature can be used to predict or monitor a subject's response to a treatment. A biosignature can be determined at different time points for a subject after initiating, removing, or altering a particular treatment.

In some embodiments, a determination or prediction as to whether a subject is responding to a treatment is made based on a change in the amount of one or more components of a biosignature (i.e., the microRNA, vesicles and/or biomarkers of interest), an amount of one or more components of a particular biosignature, or the biosignature detected for the components. In another embodiment, a subject's condition is monitored by determining a biosignature at different time points. The progression, regression, or recurrence of a condition is determined. Response to therapy can also be measured over a time course. Thus, the invention provides a method of monitoring a status of a disease or other medical condition in a subject, comprising isolating or detecting a biosignature from a biological sample from the subject, detecting the overall amount of the components of a particular biosignature, or detecting the biosignature of one or more components (such as the presence, absence, or expression level of a biomarker). The biosignatures are used to monitor the status of the disease or condition.

One or more novel biosignatures of a vesicle can also be identified. For example, one or more vesicles can be isolated from a subject that responds to a drug treatment or treatment regimen and compared to a reference, such as another subject that does not respond to the drug treatment or treatment regimen. Differences between the biosignatures can be determined and used to identify other subjects as responders or non-responders to a particular drug or treatment regimen.

In some embodiments, a biosignature is used to determine whether a particular disease or condition is resistant to a drug. If a subject is drug resistant, a physician need not waste valuable time with such drug treatment. To obtain early validation of a drug choice or treatment regimen, a biosignature is determined for a sample obtained from a subject. The biosignature is used to assess whether the particular subject's disease has the biomarker associated with drug resistance. Such a determination enables doctors to devote critical time as well as the patient's financial resources to effective treatments.

Moreover, biosignature may be used to assess whether a subject is afflicted with disease, is at risk for developing disease or to assess the stage or progression of the disease. For example, a biosignature can be used to assess whether a subject has prostate cancer (for example, FIG. 68, 73) or colon cancer (for example, FIG. 69, 74). Furthermore, a biosignature can be used to determine a stage of a disease or condition, such as colon cancer (for example, FIGS. 71, 72).

Furthermore, determining the amount of vesicles, such a heterogeneous population of vesicles, and the amount of one or more homogeneous population of vesicles, such as a population of vesicles with the same biosignature, can be used to characterize a phenotype. For example, determination of the total amount of vesicles in a sample (i.e. not cell-type specific) and determining the presence of one or more different cell-of-origin specific vesicles can be used to characterize a phenotype. Threshold values, or reference values or amounts can be determined based on comparisons of normal subjects and subjects with the phenotype of interest, as further described below, and criteria based on the threshold or reference values determined The different criteria can be used to characterize a phenotype.

One criterion can be based on the amount of a heterogeneous population of vesicles in a sample. In one embodiment, general vesicle markers, such as CD9, CD81, CD63, or a marker in Table 3, are used to determine the amount of vesicles in a sample. The expression level of any of these markers, or a combination thereof, can be detected and if the level is greater than a threshold level, the criterion is met. In another embodiment, the criterion is met if a level of any of the markers, or a combination thereof, is lower than a threshold value or reference value. In another embodiment, the criterion can be based on whether the amount of vesicles is higher than a threshold or reference value. Another criterion can be based on the amount of vesicles with a specific biosignature. If the amount of vesicles with the specific biosignature is lower than a threshold or reference value, the criterion is met. In another embodiment, if the amount of vesicles with the specific biosignature is higher than a threshold or reference value, the criterion is met. A criterion can also be based on the amount of vesicles derived from a particular cell type. If the amount is lower than a threshold or reference value, the criterion is met. In another embodiment, if the amount is higher than a threshold value, the criterion is met.

In a non-limiting example, consider that vesicles from prostate cells are determined by detecting the biomarker PCSA or PSCA, and that a criterion is met if the level of detected PCSA or PSCA is greater than a threshold level. The threshold can be the level of the same markers in a sample from a control cell line or control subject. Another criterion can be based on whether the amount of vesicles derived from a cancer cell or comprising one or more cancer specific biomarkers. For example, the biomarkers B7H3, EpCam, or both, can be determined and a criterion met if the level of detected B7H3 and/or EpCam is greater than a threshold level or within a pre-determined range. If the amount is lower, or higher, than a threshold or reference value, the criterion is met. A criterion can also be the reliability of the result, such as meeting a quality control measure or value. A detected amount of B7H3 and/or EpCam in a test sample that is above the amount of these markers in a control sample may indicate the presence of a cancer in the test sample.

As described, analysis of multiple markers can be combined to assess whether a criterion is met. In an illustrative example, a biosignature is used to assess whether a subject has prostate cancer by detecting one or more of the general vesicle markers CD9, CD63 and CD81; one or more prostate epithelial markers including PCSA or PSMA; and one or more cancer markers such as B7H3 and/or EpCam. Higher levels of the markers in a sample from a subject than in a control individual without prostate cancer indicates the presence of the prostate cancer in the subject. In some embodiments, the multiple markers are assessed in a multiplex fashion.

One of skill will understand that such rules based on meeting criterion as described can be applied to any appropriate biomarker. For example, the criterion can be applied to vesicle characteristics such as amount of vesicles present, amount of vesicles with a particular biosignature present, amount of vesicle payload biomarkers present, amount of microRNA or other circulating biomarkers present, and the like. The ratios of appropriate biomarkers can be determined. As illustrative examples, the criterion could be a ratio of an vesicle surface protein to another vesicle surface protein, a ratio of an vesicle surface protein to a microRNA, a ratio of one vesicle population to another vesicle population, a ratio of one circulating biomarker to another circulating biomarker, etc.

A phenotype for a subject can be characterized based on meeting any number of useful criteria. In some embodiments, at least one criterion is used for each biomarker. In some embodiments, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90 or at least 100 criteria are used. For example, for the characterizing of a cancer, a number of different criteria can be used when the subject is diagnosed with a cancer: 1) if the amount of microRNA in a sample from a subject is higher than a reference value; 2) if the amount of a microRNA within cell type specific vesicles (i.e. vesicles derived from a specific tissue or organ) is higher than a reference value; or 3) if the amount of microRNA within vesicles with one or more cancer specific biomarkers is higher than a reference value. Similar rules can apply if the amount of microRNA is less than or the same as the reference. The method can further include a quality control measure, such that the results are provided for the subject if the samples meet the quality control measure. In some embodiments, if the criteria are met but the quality control is questionable, the subject is reassessed.

In other embodiments, a single measure is determined for assessment of multiple biomarkers, and the measure is compared to a reference. For illustration, a test for prostate cancer might comprise multiplying the level of PSA against the level of miR-141 in a blood sample. The criterion is met if the product of the levels is above a threshold, indicating the presense of the cancer. As another illustration, a number of binding agents to general vesicle markers can carry the same label, e.g., the same fluorophore. The level of the detected label can be compared to a threshold.

Criterion can be applied to multiple types of biomarkers in addition to multiple biomarkers of the same type. For example, the levels of one or more circulating biomarkers (e.g., RNA, DNA, peptides), vesicles, mutations, etc, can be compared to a reference. Different components of a biosignature can have different criteria. As a non-limiting example, a biosignature used to diagnose a cancer can include overexpression of one miR species as compared to a reference and underexpression of a vesicle surface antigen as compared to another reference.

A biosignature can be determined by comparing the amount of vesicles, the structure of a vesicle, or any other informative characteristic of a vesicle. Vesicle structure can be assessed using transmission electron microscopy, see for example, Hansen et al., Journal of Biomechanics 31, Supplement 1: 134-134(1) (1998), or scanning electron microscopy. Various combinations of methods and techniques or analyzing one or more vesicles can be used to determine a phenotype for a subject.

A biosignature can include without limitation the presence or absence, copy number, expression level, or activity level of a biomarker. Other useful components of a biosignature include the presence of a mutation (e.g., mutations which affect activity of a transcription or translation product, such as substitution, deletion, or insertion mutations), variant, or post-translation modification of a biomarker. Post-translational modification of a protein biomarker include without limitation acylation, acetylation, phosphorylation, ubiquitination, deacetylation, alkylation, methylation, amidation, biotinylation, gamma-carboxylation, glutamylation, glycosylation, glycyation, hydroxylation, covalent attachment of heme moiety, iodination, isoprenylation, lipoylation, prenylation, GPI anchor formation, myristoylation, farnesylation, geranylgeranylation, covalent attachment of nucleotides or derivatives thereof, ADP-ribosylation, flavin attachment, oxidation, palmitoylation, pegylation, covalent attachment of phosphatidylinositol, phosphopantetheinylation, polysialylation, pyroglutamate formation, racemization of proline by prolyl isomerase, tRNA-mediation addition of amino acids such as arginylation, sulfation, the addition of a sulfate group to a tyrosine, or selenoylation of the biomarker.

The methods described herein can be used to identify a biosignature that is associated with a disease, condition or physiological state. The biosignature can also be utilized to determine if a subject is afflicted with cancer or is at risk for developing cancer. A subject at risk of developing cancer can include those who may be predisposed or who have pre-symptomatic early stage disease.

A biosignature can also be utilized to provide a diagnostic or theranostic determination for other diseases including but not limited to autoimmune diseases, inflammatory bowel diseases, cardiovascular disease, neurological diseases such as Alzheimer's disease, Parkinson's disease or Multiple Sclerosis, infectious disease such as sepsis or pancreatitis or other disease, conditions or symptoms listed in FIGS. 3-58.

The biosignature can also be used to identify a given pregnancy state from the peripheral blood, umbilical cord blood, or amniotic fluid (e.g. miRNA signature specific to Downs Syndrome) or adverse pregnancy outcome such as pre-eclampsia, pre-term birth, premature rupture of membranes, intrauterine growth restriction or recurrent pregnancy loss. The biosignature can also be used to indicate the health of the mother, the fetus at all developmental stages, the pre-implantation embryo or a newborn.

A biosignature can be utilized for pre-symptomatic diagnosis. Furthermore, the biosignature can be utilized to detect disease, determine disease stage or progression, determine the recurrence of disease, identify treatment protocols, determine efficacy of treatment protocols or evaluate the physiological status of individuals related to age and environmental exposure.

Monitoring a biosignature of a vesicle can also be used to identify toxic exposures in a subject including, but not limited to, situations of early exposure or exposure to an unknown or unidentified toxic agent. Without being bound by any one specific theory for mechanism of action, vesicles can shed from damaged cells and in the process compartmentalize specific contents of the cell including both membrane components and engulfed cytoplasmic contents. Cells exposed to toxic agents/chemicals may increase vesicle shedding to expel toxic agents or metabolites thereof, thus resulting in increased vesicle levels. Thus, monitoring vesicle levels, vesicle biosignature, or both, allows assessment of an individual's response to potential toxic agent(s).

A vesicle and/or other biomarkers of the invention can be used to identify states of drug-induced toxicity or the organ injured, by detecting one or more specific antigen, binding agent, biomarker, or any combination thereof. The level of vesicles, changes in the biosignature of a vesicle, or both, can be used to monitor an individual for acute, chronic, or occupational exposures to any number of toxic agents including, but not limited to, drugs, antibiotics, industrial chemicals, toxic antibiotic metabolites, herbs, household chemicals, and chemicals produced by other organisms, either naturally occurring or synthetic in nature. In addition, a biosignature can be used to identify conditions or diseases, including cancers of unknown origin, also known as cancers of unknown primary (CUP).

A vesicle may be isolated from a biological sample as previously described to arrive at a heterogeneous population of vesicles. The heterogeneous population of vesicles can then be contacted with substrates coated with specific binding agents designed to rule out or identify antigen specific characteristics of the vesicle population that are specific to a given cell-of-origin. Further, as described above, the biosignature of a vesicle can correlate with the cancerous state of cells. Compounds that inhibit cancer in a subject may cause a change, e.g., a change in biosignature of a vesicle, which can be monitored by serial isolation of vesicles over time and treatment course. The level of vesicles or changes in the level of vesicles with a specific biosignature can be monitored.

In an aspect, characterizing a phenotype of a subject comprises a method of determining whether the subject is likely to respond or not respond to a therapy. The methods of the invention also include determining new biosignatures useful in predicting whether the subject is likely to respond or not. One or more subjects that respond to a therapy (responders) and one or more subjects that do not respond to the same therapy (non-responders) can have their vesicles interrogated. Interrogation can be performed to identify vesicle biosignatures that classify a subject as a responder or non-responder to the treatment of interest. In some aspects, the presence, quantity, and payload of a vesicle are assayed. The payload of a vesicle includes, for example, internal proteins, nucleic acids such as miRNA, lipids or carbohydrates.

A biosignature indicative of responder/non-responder status can be used for theranosis. A sample from subjects with known or determinable responder/non-responder status may be analyzed for one or more of the following: amount of vesicles, amount of a unique subset or species of vesicles, biomarkers in such vesicles, biosignature of such vesicles, etc. In one instance, vesicles such as microvesicles or exosomes from responders and non-responders are analyzed for the presence and/or quantity of one or more miRNAs, such as miRNA 122, miR-548c-5p, miR-362-3p, miR-422a, miR-597, miR-429, miR-200a, and/or miR-200b. A difference in biosignatures between responders and non-responders can be used for theranosis. In another embodiment, vesicles are obtained from subjects having a disease or condition. Vesicles are also obtained from subjects free of such disease or condition. The vesicles from both groups of subjects are assayed for unique biosignatures that are associated with all subjects in that group but not in subjects from the other group. Such biosignatures or biomarkers can then used as a diagnostic for the presence or absence of the condition or disease, or to classify the subject as belonging on one of the groups (those with/without disease, aggressive/non-aggressive disease, responder/non-responder, etc).

In an aspect, characterizing a phenotype of a subject comprises a method of staging a disease. The methods of the invention also include determining new biosignatures useful in staging. In an illustrative example, vesicles are assayed from patients having a stage I cancer and patients having stage II or stage III of the same cancer. In some embodiments, vesicles are assayed in patients with metastatic disease. A difference in biosignatures or biomarkers between vesicles from each group of patient is identified (e.g., vesicles from stage III cancer may have an increased expression of one or more genes or miRNA's), thereby identifying a biosignature or biomarker that distinguishes different stages of a disease. Such biosignature can then be used to stage patients having the disease.

In some instances, a biosignature is determined by assaying vesicles from a subject over a period of time, e.g., daily, semiweekly, weekly, biweekly, semimonthly, monthly, bimonthly, semiquarterly, quarterly, semiyearly, biyearly or yearly. For example, the biosignatures in patients on a given therapy can be monitored over time to detect signatures indicative of responders or non-responders for the therapy. Similarly, patients with differing stages of disease have their vesicles interrogated over time. The payload or physical attributes of the vesicles in each point in time can be compared. A temporal pattern can thus form a biosignature that can then be used for theranosis, diagnosis, prognosis, disease stratification, treatment monitoring, disease monitoring or making a prediction of responder/non-responder status. As an illustrative example only, an increasing amount of a biomarker (e.g., miR 122) in vesicles over a time course is associated with metastatic cancer, as opposed to a stagnant amounts of the biomarker in vesicles over the time course that are associated with non-metastatic cancer. A time course may last over at least 1 week, 2 weeks, 3 weeks, 4 weeks, 1 month, 6 weeks, 8 weeks, 2 months, 10 weeks, 12 weeks, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, one year, 18 months, 2 years, or at least 3 years.

The level of vesicles, level of vesicles with a specific biosignature, or a biosignature of a vesicle can also be used to assess the efficacy of a therapy for a condition. For example, the level of vesicles, level of vesicles with a specific biosignature, or a biosignature of a vesicle can be used to assess the efficacy of a cancer treatment, e.g., chemotherapy, radiation therapy, surgery, or any other therapeutic approach useful for inhibiting cancer in a subject. In addition, a biosignature can be used in a screening assay to identify candidate or test compounds or agents (e.g., proteins, peptides, peptidomimetics, peptoids, small molecules or other drugs) that have a modulatory effect on the biosignature of a vesicle. Compounds identified via such screening assays may be useful, for example, for modulating, e.g., inhibiting, ameliorating, treating, or preventing conditions or diseases.

For example, a biosignature for a vesicle can be obtained from a patient who is undergoing successful treatment for a particular cancer. Cells from a cancer patient not being treated with the same drug can be cultured and vesicles from the cultures obtained for determining biosignatures. The cells can be treated with test compounds and the biosignature of the vesicles from the cultures can be compared to the biosignature of the vesicles obtained from the patient undergoing successful treatment. The test compounds that results in biosignatures that are similar to those of the patient undergoing successful treatment can be selected for further studies.

The biosignature of a vesicle can also be used to monitor the influence of an agent (e.g., drug compounds) on the biosignature in clinical trials. Monitoring the level of vesicles, changes in the biosignature of a vesicle, or both, can also be used in a method of assessing the efficacy of a test compound, such as a test compound for inhibiting cancer cells.

In addition to diagnosing or confirming the presence of or risk for developing a disease, condition or a syndrome, the methods and compositions disclosed herein also provide a system for optimizing the treatment of a subject having such a disease, condition or syndrome. The level of vesicles, the biosignature of a vesicle, or both, can also be used to determine the effectiveness of a particular therapeutic intervention (pharmaceutical or non-pharmaceutical) and to alter the intervention to 1) reduce the risk of developing adverse outcomes, 2) enhance the effectiveness of the intervention or 3) identify resistant states. Thus, in addition to diagnosing or confirming the presence of or risk for developing a disease, condition or a syndrome, the methods and compositions disclosed herein also provide a system for optimizing the treatment of a subject having such a disease, condition or syndrome. For example, a therapy-related approach to treating a disease, condition or syndrome by integrating diagnostics and therapeutics to improve the real-time treatment of a subject can be determined by identifying the biosignature of a vesicle.

Tests that identify the level of vesicles, the biosignature of a vesicle, or both, can be used to identify which patients are most suited to a particular therapy, and provide feedback on how well a drug is working, so as to optimize treatment regimens. For example, in pregnancy-induced hypertension and associated conditions, therapy-related diagnostics can flexibly monitor changes in important parameters (e.g., cytokine and/or growth factor levels) over time, to optimize treatment.

Within the clinical trial setting of investigational agents as defined by the FDA, MDA, EMA, USDA, and EMEA, therapy-related diagnostics as determined by a biosignature disclosed herein, can provide key information to optimize trial design, monitor efficacy, and enhance drug safety. For instance, for trial design, therapy-related diagnostics can be used for patient stratification, determination of patient eligibility (inclusion/exclusion), creation of homogeneous treatment groups, and selection of patient samples that are optimized to a matched case control cohort. Such therapy-related diagnostic can therefore provide the means for patient efficacy enrichment, thereby minimizing the number of individuals needed for trial recruitment. For example, for efficacy, therapy-related diagnostics are useful for monitoring therapy and assessing efficacy criteria. Alternatively, for safety, therapy-related diagnostics can be used to prevent adverse drug reactions or avoid medication error and monitor compliance with the therapeutic regimen.

In some embodiments, the invention provides a method of identifying responder and non-responders to a treatment undergoing clinical trials, comprising detecting biosignatures in subjects enrolled in the clinical trial, and identifying biosignatures that distinguish between responders and non-responders. In a further embodiment, the biosignatures are measured in a drug naive subject and used to predict whether the subject will be a responder or non-responder. The prediction can be based upon whether the biosignatures of the drug naive subject correlate more closely with the clinical trial subjects identified as responders, thereby predicting that the drug naive subject will be a responder. Conversely, if the biosignatures of the drug naive subject correlate more closely with the clinical trial subjects identified as non-responders, the methods of the invention can predict that the drug naive subject will be a non-responder. The prediction can therefore be used to stratify potential responders and non-responders to the treatment. In some embodiments, the prediction is used to guide a course of treatment, e.g., by helping treating physicians decide whether to administer the drug. In some embodiments, the prediction is used to guide selection of patients for enrollment in further clinical trials. In a non-limiting example, biosignatures that predict responder/non-responder status in Phase II trials can be used to select patients for a Phase III trial, thereby increasing the likelihood of response in the Phase III patient population. One of skill will appreciate that the method can be adapted to identify biosignatures to stratify subjects on criteria other than responder/non-responder status. In one embodiment, the criterion is treatment safety. Therefore the method is followed as above to identify subjects who are likely or not to have adverse events to the treatment. In a non-limiting example, biosignatures that predict safety profile in Phase II trials can be used to select patients for a Phase III trial, thereby increasing the treatment safety profile in the Phase III patient population.

Therefore, biosignatures based on circulating biomarkers can be used to monitor drug efficacy, determine response or resistance to a given drug, or both, thereby enhancing drug safety. For example, in colon cancer, vesicles are typically shed from colon cancer cells and can be isolated from the peripheral blood and used to isolate one or more biomarkers e.g., KRAS mRNA which can then be sequenced to detect KRAS mutations. In the case of mRNA biomarkers, the mRNA can be reverse transcribed into cDNA and sequenced (e.g., by Sanger sequencing, pyrosequencing, NextGen sequencing, RT-PCR assays) to determine if there are mutations present that confer resistance to a drug (e.g., cetuximab or panitumimab). In another example, vesicles that are specifically shed from lung cancer cells are isolated from a biological sample and used to isolate a lung cancer biomarker, e.g., EGFR mRNA. The EGFR mRNA is processed to cDNA and sequenced to determine if there are EGFR mutations present that show resistance or response to specific drugs or treatments for lung cancer.

One or more biosignatures can be grouped so that information obtained about the set of biosignatures in a particular group provides a reasonable basis for making a clinically relevant decision, such as but not limited to a diagnosis, prognosis, or management of treatment, such as treatment selection.

As with most diagnostic markers, it is often desirable to use the fewest number of markers sufficient to make a correct medical judgment. This prevents a delay in treatment pending further analysis as well inappropriate use of time and resources.

Also disclosed herein are methods of conducting retrospective analysis on samples (e.g., serum and tissue biobanks) for the purpose of correlating qualitative and quantitative properties, such as biosignatures of vesicles, with clinical outcomes in terms of disease state, disease stage, progression, prognosis; therapeutic efficacy or selection; or physiological conditions. Furthermore, methods and compositions disclosed herein are utilized for conducting prospective analysis on a sample (e.g., serum and/or tissue collected from individuals in a clinical trial) for the purpose of correlating qualitative and quantitative biosignatures of vesicles with clinical outcomes in terms of disease state, disease stage, progression, prognosis; therapeutic efficacy or selection; or physiological conditions can also be performed. As used herein, a biosignature for a vesicle can be used to identify a cell-of-origin specific vesicle. Furthermore, a biosignature can be determined based on a surface marker profile of a vesicle or contents of a vesicle.

The biosignatures used to characterize a phenotype according to the invention can comprise multiple components (e.g., microRNA, vesicles or other biomarkers) or characteristics (e.g., vesicle size or morphology). The biosignatures can comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 75, or 100 components or characteristics. A biosignature with more than one component or characteristic, such as at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 75, or 100 components, may provide higher sensitivity and/or specificity in characterizing a phenotype. In some embodiments, assessing a plurality of components or characteristics provides increased sensitivity and/or specificity as compared to assessing fewer components or characteristics. On the other hand, it is often desirable to use the fewest number of components or characteristics sufficient to make a correct medical judgment. Fewer markers can avoid statistical overfitting of a classifier and can prevent a delay in treatment pending further analysis as well inappropriate use of time and resources. Thus, the methods of the invention comprise determining an optimal number of components or characteristics.

A biosignature according to the invention can be used to characterize a phenotype with a sensitivity, specificity, accuracy, or similar performance metric as described above. The biosignatures can also be used to build a classifier to classify a sample as belonging to a group, such as belonging to a group having a disease or not, a group having an aggressive disease or not, or a group of responders or non-responders. In one embodiment, a classifier is used to determine whether a subject has an aggressive or non-aggressive cancer. In the illustrative case of prostate cancer, this can help a physician to determine whether to watch the cancer, i.e., prescribe “watchful waiting,” or perform a prostatectomy. In another embodiment, a classifier is used to determine whether a breast cancer patient is likely to respond or not to tamoxifen, thereby helping the physician to determine whether or not to treat the patient with tamoxifen or another drug.

Biomarkers

A biosignature used to characterize a phenotype can comprise one or more biomarkers. The biomarker can be a circulating marker, a membrane associated marker, or a component present within a vesicle or on a vesicle's surface. These biomarkers include without limitation a nucleic acid (e.g. RNA (mRNA, miRNA, etc.) or DNA), protein, peptide, polypeptide, antigen, lipid, carbohydrate, or proteoglycan.

The biosignature can include the presence or absence, expression level, mutational state, genetic variant state, or any modification (such as epigenetic modification or post-translational modification) of a biomarker (e.g. any one or more biomarker listed in FIGS. 1, 3-60). The expression level of a biomarker can be compared to a control or reference, to determine the overexpression or underexpression (or upregulation or downregulation) of a biomarker in a sample. In some embodiments, the control or reference level comprises the amount of a same biomarker, such as a miRNA, in a control sample from a subject that does not have or exhibit the condition or disease. In another embodiment, the control of reference levels comprises that of a housekeeping marker whose level is minimally affected, if at all, in different biological settings such as diseased versus non-diseased states. In yet another embodiment, the control or reference level comprises that of the level of the same marker in the same subject but in a sample taken at a different time point. Other types of controls are described herein.

Nucleic acid biomarkers include various RNA or DNA species. For example, the biomarker can be mRNA, microRNA (miRNA or miRs), small nucleolar RNAs (snoRNA), small nuclear RNAs (snRNA), ribosomal RNAs (rRNA), heterogeneous nuclear RNA (hnRNA), ribosomal RNAS (rRNA), siRNA, transfer RNAs (tRNA), or shRNA. The DNA can be double-stranded DNA, single stranded DNA, complementary DNA, or noncoding DNA. miRNAs are short ribonucleic acid (RNA) molecules which average about 22 nucleotides long. miRNAs act as post-transcriptional regulators that bind to complementary sequences in the three prime untranslated regions (3′ UTRs) of target messenger RNA transcripts (mRNAs), which can result in gene silencing. One miRNA may act upon 1000s of mRNAs. miRNAs play multiple roles in negative regulation, e.g., transcript degradation and sequestering, translational suppression, and may also have a role in positive regulation, e.g., transcriptional and translational activation. By affecting gene regulation, miRNAs can influence many biologic processes. Different sets of expressed miRNAs are found in different cell types and tissues.

Biomarkers for use with the invention further include peptides, polypeptides, or proteins, which terms are used interchangeably throughout unless otherwise noted. In some embodiments, the protein biomarker comprises its modification state, truncations, mutations, expression level (such as overexpression or underexpression as compared to a reference level), and/or post-translational modifications, such as described above. In a non-limiting example, a biosignature for a disease can include a protein having a certain post-translational modification that is more prevalent in a sample associated with the disease than without.

A biosignature may include a number of the same type of biomarkers (e.g., one or more different microRNA or mRNA species) or one or more of different types of biomarkers (e.g. mRNAs, miRNAs, proteins, peptides, ligands, and antigens).

One or more biosignatures can comprise at least one biomarker selected from those listed in FIGS. 1, 3-60. A specific cell-of-origin biosignature may include one or more biomarkers. FIGS. 3-58 depict tables which lists a number of disease or condition specific biomarkers that can be derived and analyzed from a vesicle. The biomarker can also be CD24, midkine, hepcidin, TMPRSS2-ERG, PCA-3, PSA, EGFR, EGFRvIII, BRAF variant, MET, cKit, PDGFR, Wnt, beta-catenin, K-ras, H-ras, N-ras, Raf, N-myc, c-myc, IGFR, PI3K, Akt, BRCA1, BRCA2, PTEN, VEGFR-2, VEGFR-1, Tie-2, TEM-1, CD276, HER-2, HER-3, or HER-4. The biomarker can also be annexin V, CD63, Rab-5b, or caveolin, or a miRNA, such as let-7a; miR-15b; miR-16; miR-19b; miR-21; miR-26a; miR-27a; miR-92; miR-93; miR-320 or miR-20. The biomarker can also be of any gene or fragment thereof as disclosed in PCT Publication No. WO/2009/100029, such as those listed in Tables 3-15 therein.

In another embodiment, a vesicle comprises a cell fragment or cellular debris derived from a rare cell, such as described in PCT Publication No. WO2006054991. One or more biomarkers, such as CD 146, CD 105, CD31, CD 133, CD 106, or a combination thereof, can be assessed for the vesicle. In one embodiment, a capture agent for the one or more biomarkers is used to isolate or detect a vesicle. In some embodiments, one or more of the biomarkers CD45, cytokeratin (CK) 8, CK18, CK19, CK20, CEA, EGFR, GUC, EpCAM, VEGF, TS, Muc-1, or a combination thereof is assessed for a vesicle. In one embodiment, a tumor-derived vesicle is CD45−, CK+ and comprises a nucleic acid, wherein the membrane vesicle has an absence of, or low expression or detection of CD45, has detectable expression of a cytokeratin (such as CK8, CK18, CK19, or CK20), and detectable expression of a nucleic acid.

Any number of useful biomarkers that can be assessed as part of a vesicle biosignature are disclosed throughout the application, including without limitation CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR, or a combination thereof.

Other biomarkers useful for assessment in methods and compositions disclosed herein include those associated with conditions or physiological states as disclosed in U.S. Pat. Nos. 6,329,179 and 7,625,573; U.S. Patent Publication Nos. 2002/106684, 2004/005596, 2005/0159378, 2005/0064470, 2006/116321, 2007/0161004, 2007/0077553, 2007/104738, 2007/0298118, 2007/0172900, 2008/0268429, 2010/0062450, 2007/0298118, 2009/0220944 and 2010/0196426; U.S. patent application Ser. Nos. 12/524,432, 12/524,398, 12/524,462; Canadian Patent CA 2453198; and International PCT Patent Publication Nos. WO1994022018, WO2001036601, WO2003063690, WO2003044166, WO2003076603, WO2005121369, WO2005118806, WO/2005/078124, WO2007126386, WO2007088537, WO2007103572, WO2009019215, WO2009021322, WO2009036236, WO2009100029, WO2009015357, WO2009155505, WO 2010/065968 and WO 2010/070276; each of which patent or application is incorporated herein by reference in their entirety. The biomarkers disclosed in these patents and applications, including vesicle biomarkers and microRNAs, can be assessed as part of a signature for characterizing a phenotype, such as providing a diagnosis, prognosis or theranosis of a cancer or other disease. Furthermore, the methods and techniques disclosed therein can be used to assess biomarkers, including vesicle biomarkers and microRNAs.

Another group of useful biomarkers for assessment in methods and compositions disclosed herein include those associated with cancer diagnostics, prognostics and theranostics as disclosed in U.S. Pat. Nos. 6,692,916, 6,960,439, 6,964,850, 7,074,586; U.S. patent application Ser. Nos. 11/159,376, 11/804,175, 12/594,128, 12/514,686, 12/514,775, 12/594,675, 12/594,911, 12/594,679, 12/741,787, 12/312,390; and International PCT Patent Application Nos. PCT/US2009/049935, PCT/US2009/063138, PCT/US2010/000037; each of which patent or application is incorporated herein by reference in their entirety. Usefule biomarkers further include those described in U.S. patent application Ser. No. 10/703,143 and Ser. No. 10/701,391 for inflammatory disease; Ser. No. 11/529,010 for rheumatoid arthritis; Ser. No. 11/454,553 and Ser. No. 11/827,892 for multiple sclerosis; Ser. No. 11/897,160 for transplant rejection; Ser. No. 12/524,677 for lupus; PCT/US2009/048684 for osteoarthritis; Ser. No. 10/742,458 for infectious disease and sepsis; Ser. No. 12/520,675 for sepsis; each of which patent or application is incorporated herein by reference in their entirety. The biomarkers disclosed in these patents and applications, including mRNAs, can be assessed as part of a signature for characterizing a phenotype, such as providing a diagnosis, prognosis or theranosis of a cancer or other disease. Furthermore, the methods and techniques disclosed therein can be used to assess biomarkers, including vesicle biomarkers and microRNAs.

Still other biomarkers useful for assessment in methods and compositions disclosed herein include those associated with conditions or physiological states as disclosed in Wieczorek et al., Isolation and characterization of an RNA-proteolipid complex associated with the malignant state in humans, Proc Natl Acad Sci USA. 1985 May; 82(10):3455-9; Wieczorek et al., Diagnostic and prognostic value of RNA-proteolipid in sera of patients with malignant disorders following therapy: first clinical evaluation of a novel tumor marker, Cancer Res. 1987 Dec. 1; 47(23):6407-12; Escola et al. Selective enrichment of tetraspan proteins on the internal vesicles of multivesicular endosomes and on exosomes secreted by human B-lymphocytes. J. Biol. Chem. (1998) 273:20121-27; Pileri et al. Binding of hepatitis C virus to CD81 Science, (1998) 282:938-41); Kopreski et al. Detection of Tumor Messenger RNA in the Serum of Patients with Malignant Melanoma, Clin. Cancer Res. (1999) 5:1961-1965; Can et al. Circulating Membrane Vesicles in Leukemic Blood, Cancer Research, (1985) 45:5944-51; Weichert et al. Cytoplasmic CD24 expression in colorectal cancer independently correlates with shortened patient survival. Clinical Cancer Research, 2005, 11:6574-81); Iorio et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res (2005) 65:7065-70; Taylor et al. Tumour-derived exosomes and their role in cancer-associated T-cell signaling defects British J Cancer (2005) 92:305-11; Valadi et al. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells Nature Cell Biol (2007) 9:654-59; Taylor et al. Pregnancy-associated exosomes and their modulation of T cell signaling J Immunol (2006) 176:1534-42; Koga et al. Purification, characterization and biological significance of tumor-derived exosomes Anticancer Res (2005) 25:3703-08; Seligson et al. Epithelial cell adhesion molecule (KSA) expression: pathobiology and its role as an independent predictor of survival in renal cell carcinoma Clin Cancer Res (2004) 10:2659-69; Clayton et al. (Antigen-presenting cell exosomes are protected from complement-mediated lysis by expression of CD55 and CD59. Eur J Immunol (2003) 33:522-31); Simak et al. Cell Membrane Microparticles in Blood and Blood Products: Potentially Pathogenic Agents and Diagnostic Markers Trans Med Reviews (2006) 20:1-26; Choi et al. Proteomic analysis of microvesicles derived from human colorectal cancer cells J Proteome Res (2007) 6:4646-4655; Iero et al. Tumour-released exosomes and their implications in cancer immunity Cell Death Diff (2008) 15:80-88; Baj-Krzyworzeka et al. Tumour-derived microvesicles carry several surface determinants and mRNA of tumour cells and transfer some of these determinants to monocytes Cencer Immunol Immunother (2006) 55:808-18; Admyre et al. B cell-derived exosomes can present allergen peptides and activate allergen-specific T cells to proliferate and produce TH2-like cytokines J Allergy Clin Immunol (2007) 120:1418-1424; Aoki et al. Identification and characterization of microvesicles secreted by 3T3-L1 adipocytes: redox-and hormone dependent induction of milk fat globule-epidermal growth factor 8-associated microvesicles Endocrinol (2007) 148:3850-3862; Baj-Krzyworzeka et al. Tumour-derived microvesicles carry several surface determinants and mRNA of tumour cells and transfer some of these determinants to monocytes Cencer Immunol Immunother (2006) 55:808-18; Skog et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers Nature Cell Biol (2008) 10:1470-76; El-Hefnawy et al. Characterization of amplifiable, circulating RNA in plasma and its potential as a tool for cancer diagnostics Clin Chem (2004) 50:564-573; Pisitkun et al., Proc Natl Acad Sci USA, 2004; 101:13368-13373; Mitchell et al., Can urinary exosomes act as treatment response markers in Prostate Cancer?, Journal of Translational Medicine 2009, 7:4; Clayton et al., Human Tumor-Derived Exosomes Selectively Impair Lymphocyte Responses to Interleukin-2, Cancer Res 2007; 67: (15). Aug. 1, 2007; Rabesandratana et al. Decay-accelerating factor (CD55) and membrane inhibitor of reactive lysis (CD59) are released within exosomes during In vitro maturation of reticulocytes. Blood 91:2573-2580 (1998); Lamparski et al. Production and characterization of clinical grade exosomes derived from dendritic cells. J Immunol Methods 270:211-226 (2002); Keller et al. CD24 is a marker of exosomes secreted into urine and amniotic fluid. Kidney Int'l 72:1095-1102 (2007); Runz et al. Malignant ascites-derived exosomes of ovarian carcinoma patients contain CD24 and EpCAM. Gyn Oncol 107:563-571 (2007); Redman et al. Circulating microparticles in normal pregnancy and preeclampsia placenta. 29:73-77 (2008); Gutwein et al. Cleavage of L 1 in exosomes and apoptotic membrane vesicles released from ovarian carcinoma cells. Clin Cancer Res 11:2492-2501 (2005); Kristiansen et al., CD24 is an independent prognostic marker of survival in nonsmall cell lung cancer patients, Brit J Cancer 88:231-236 (2003); Lim and Oh, The Role of CD24 in Various Human Epithelial Neoplasias, Pathol Res Pract 201:479-86 (2005); Matutes et al., The Immunophenotype of Splenic Lymphoma with Villous Lymphocytes and its Relevance to the Differential Diagnosis With Other B-Cell Disorders, Blood 83:1558-1562 (1994); Pirruccello and Lang, Differential Expression of CD24-Related Epitopes in Mycosis Fungoides/Sezary Syndrome: A Potential Marker for Circulating Sezary Cells, Blood 76:2343-2347 (1990). The biomarkers disclosed in these publications, including vesicle biomarkers and microRNAs, can be assessed as part of a signature for characterizing a phenotype, such as providing a diagnosis, prognosis or theranosis of a cancer or other disease. Furthermore, the methods and techniques disclosed therein can be used to assess biomarkers, including vesicle biomarkers and microRNAs.

Still other biomarkers useful for assessment in methods and compositions disclosed herein include those associated with conditions or physiological states as disclosed in Rajendran et al., Proc Natl Acad Sci USA 2006; 103:11172-11177, Taylor et al., Gynecol Oncol 2008; 110:13-21, Zhou et al., Kidney Int 2008; 74:613-621, Buning et al., Immunology 2008, Prado et al. J Immunol 2008; 181:1519-1525, Vella et al. (2008) Vet Immunol Immunopathol 124(3-4): 385-93, Gould et al. (2003). Proc Natl Acad Sci USA 100(19): 10592-7, Fang et al. (2007). PLoS Biol 5(6): e158, Chen, B. J and R. A. Lamb (2008). Virology 372(2): 221-32, Bhatnagar, S, and J. S. Schorey (2007). J Biol Chem 282(35): 25779-89, Bhatnagar et al. (2007) Blood 110(9): 3234-44, Yuyama, et al. (2008). J Neurochem 105(1): 217-24, Gomes et al. (2007). Neurosci Lett 428(1): 43-6, Nagahama et al. (2003). Autoimmunity 36(3): 125-31, Taylor, D. D., S. Akyol, et al. (2006). J Immunol 176(3): 1534-42, Peche, et al. (2006). Am J Transplant 6(7): 1541-50, Zero, M., M. Valenti, et al. (2008). Cell Death and Differentiation 15: 80-88, Gesierich, S., I. Berezoversuskiy, et al. (2006), Cancer Res 66(14): 7083-94, Clayton, A., A. Turkes, et al. (2004). Faseb J 18(9): 977-9, Skriner., K. Adolph, et al. (2006). Arthritis Rheum 54(12): 3809-14, Brouwer, R., G. J Pruijn, et al. (2001). Arthritis Res 3(2): 102-6, Kim, S. H, N Bianco, et al. (2006). Mol Ther 13(2): 289-300, Evans, C. H, S. C. Ghivizzani, et al. (2000). Clin Orthop Relat Res (379 Suppl): S300-7, Zhang, H G., C. Liu, et al. (2006). J Immunol 176(12): 7385-93, Van Niel, G., J. Mallegol, et al. (2004). Gut 52: 1690-1697, Fiasse, R. and O. Dewit (2007). Expert Opinion on Therapeutic Patents 17(12): 1423-1441(19). The biomarkers disclosed in these publications, including vesicle biomarkers and microRNAs, can be assessed as part of a signature for characterizing a phenotype, such as providing a diagnosis, prognosis or theranosis of a cancer or other disease. Furthermore, the methods and techniques disclosed therein can be used to assess biomarkers, including vesicle biomarkers and microRNAs.

In another aspect, the invention provides a method of assessing a cancer comprising detecting a level of one or more circulating biomarkers in a sample from a subject selected from the group consisting of CD9, HSP70, Gal3, MIS, EGFR, ER, ICB3, CD63, B7H4, MUC1, DLL4, CD81, ERB3, VEGF, BCA225, BRCA, CA125, CD174, CD24, ERB2, NGAL, GPR30, CYFRA21, CD31, cMET, MUC2 or ERB4. CD9, HSP70, Gal3, MIS, EGFR, ER, ICB3, CD63, B7H4, MUC1, DLL4, CD81, ERB3, VEGF, BCA225, BRCA, BCA200, CA125, CD174, CD24, ERB2, NGAL, GPR30, CYFRA21, CD31, cMET, MUC2 or ERB4. In another embodiment, the one or more circulating biomarkers are selected from the group consisting of CD9, EphA2, EGFR, B7H3, PSMA, PCSA, CD63, STEAP, STEAP, CD81, B7H3, STEAP1, ICAM1 (CD54), PSMA, A33, DR3, CD66e, MFG-8e, EphA2, Hepsin, TMEM211, EphA2, TROP-2, EGFR, Mammoglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, NK-2, EpCam, NGAL, NK-1R, PSMA, 5T4, PAI-1, and CD45. In still another embodiment, the one or more circulating biomarkers are selected from the group consisting of CD9, MIS Rii, ER, CD63, MUC1, HER3, STAT3, VEGFA, BCA, CA125, CD24, EPCAM, and ERB B4. Any number of useful biomarkers can be assessed from these groups, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. In some embodiments, the one or more biomarkers are one or more of Gal3, BCA200, OPN and NCAM, e.g., Gal3 and BCA200, OPN and NCAM, or all four. Assessing the cancer may comprise diagnosing, prognosing or theranosing the cancer. The cancer can be a breast cancer. The markers can be associated with a vesicle or vesicle population. For example, the one or more circulating biomarker can be a vesicle surface antigen or vesicle payload. Vesicle surface antigens can further be used as capture antigens, detector antigens, or both.

The invention further provides a method of predicted response to a therapeutic agent comprising detecting a level of one or more circulating biomarkers in a sample from a subject selected from the group consisting of CD9, HSP70, Gal3, MIS, EGFR, ER, ICB3, CD63, B7H4, MUC1, DLL4, CD81, ERB3, VEGF, BCA225, BRCA, CA125, CD174, CD24, ERB2, NGAL, GPR30, CYFRA21, CD31, cMET, MUC2 or ERB4. In another embodiment, the one or more circulating biomarkers are selected from the group consisting of CD9, EphA2, EGFR, B7H3, PSMA, PCSA, CD63, STEAP, STEAP, CD81, B7H3, STEAP1, ICAM1 (CD54), PSMA, A33, DR3, CD66e, MFG-8e, EphA2, Hepsin, TMEM211, EphA2, TROP-2, EGFR, Mammoglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, NK-2, EpCam, NGAL, NK-1R, PSMA, 5T4, PAI-1, and CD45. In still another embodiment, the one or more circulating biomarkers are selected from the group consisting of CD9, MIS Rii, ER, CD63, MUC1, HER3, STAT3, VEGFA, BCA, CA125, CD24, EPCAM, and ERB B4. Any number of useful biomarkers can be assessed from these groups, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. In some embodiments, the one or more biomarkers are one or more of Gal3, BCA200, OPN and NCAM, e.g., Gal3 and BCA200, OPN and NCAM, or all four. The therapeutic agent can be a therapeutic agent for treating cancer. The cancer can be a breast cancer. The markers can be associated with a vesicle or vesicle population. For example, the one or more circulating biomarker can be a vesicle surface antigen or vesicle payload. Vesicle surface antigens can further be used as capture antigens, detector antigens, or both.

The one or more biomarkers can be detected using an antibody array, microbeads, or other method disclosed herein or known in the art. For example, a capture antibody or aptamer to the one or more biomarkers can be bound to the array or bead. The captured vesicles can then be detected using a detectable agent. In some embodiments, captured vesicles are detected using an agent, e.g., an antibody or aptamer, that recognizes general vesicle biomarkers that detect the overall population of vesicles, such as a tetraspanin or MFG-E8. These can include tetraspanins such as CD9, CD63 and/or CD81. In other embodiments, the captured vesicles are detected using markers specific for vesicle origin, e.g., a type of tissue or organ. In some embodiments, the captured vesicles are detected using CD31, a marker for cells or vesicles of endothelial origin. As desired, the biomarkers used for capture can also be used for detection, and vice versa.

In an aspect, the invention provides a method of assessing a cancer comprising detecting a level of one or more circulating biomarker in a sample from a subject selected from the group consisting of 5T4 (trophoblast), ADAM10, AGER/RAGE, APC, APP (β-amyloid), ASPH (A-10), B7H3 (CD276), BACE1, BAI3, BRCA1, BDNF, BIRC2, C1GALT1, CA125 (MUC16), Calmodulin 1, CCL2 (MCP-1), CD9, CD10, CD127 (IL7R), CD174, CD24, CD44, CD63, CD81, CEA, CRMP-2, CXCR3, CXCR4, CXCR6, CYFRA 21, derlin 1, DLL4, DPP6, E-CAD, EpCaM, EphA2 (H-77), ER(1) ESR1 α, ER(2) ESR2 β, Erb B4, Erbb2, erb3 (Erb-B3), PA2G4, FRT (FLT1), Gal3, GPR30 (G-coupled ER1), HAP1, HER3, HSP-27, HSP70, IC3b, IL8, insig, junction plakoglobin, Keratin 15, KRAS, Mammaglobin, MART1, MCT2, MFGE8, MMP9, MRP8, Muc1, MUC17, MUC2, NCAM, NG2 (CSPG4), Nga1, NHE-3, NT5E (CD73), ODC1, OPG, OPN, p53, PARK7, PCSA, PGP9.5 (PARKS), PR(B), PSA, PSMA, RAGE, STXBP4, Survivin, TFF3 (secreted), TIMP1, TIMP2, TMEM211, TRAF4 (scaffolding), TRAIL-R2 (death Receptor 5), TrkB, Tsg 101, UNC93a, VEGF A, VEGFR2, YB-1, VEGFR1, GCDPF-15 (PIP), BigH3 (TGFb1-induced protein), 5HT2B (serotonin receptor 2B), BRCA2, BACE 1, CDH1-cadherin. The detected biomarker can comprise protein, RNA or DNA. The one or more marker can be associated with a vesicle, e.g., as a vesicle surface antigen or as vesicle payload (e.g., soluble protein, mRNA or DNA). Any number of useful biomarkers can be assessed from the group, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. The cancer can be a breast cancer. The markers can be associated with a vesicle or vesicle population. For example, the one or more circulating biomarker can be a vesicle surface antigen or vesicle payload. Vesicle surface antigens can further be used as capture antigens, detector antigens, or both.

The invention also provides a method of assessing a cancer, comprising detecting in a sample from a subject a level of one or more circulating biomarker for immunomodulation, one or more circulating biomarker for metastasis, and one or more circulating biomarker for angiogenesis; and comparing the level to a reference, thereby assessing the cancer. The one or more circulating biomarker for immunomodulation can be one or more of CD45, FasL, CTLA4, CD80 and CD83. The one or more circulating biomarker for metastatis can be one or more of Muc1, CD147, TIMP1, TIMP2, MMP7, and MMP9. The one or more circulating biomarker for angiogenesis can be one or more of HIF2a, Tie2, Ang1, DLL4 and VEGFR2. Any number of useful biomarkers can be assessed from the groups, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. The cancer can be a breast cancer. The markers can be associated with a vesicle or vesicle population. For example, the one or more circulating biomarker can be a vesicle surface antigen or vesicle payload. Vesicle surface antigens can further be used as capture antigens, detector antigens, or both.

In some embodiments, the one or more biomarkers comprise DLL4 or cMET. Delta-like 4 (DLL4) is a Notch-ligand and is up-regulated during angiogenesis. cMET (also referred to as c-Met, MET, or MNNG HOS Transforming gene) is a proto-oncogene that encodes a membrane receptor tyrosine kinase whose ligand is hepatocyte growth factor (HGF). The MET protein is sometimes referred to as the hepatocyte growth factor receptor (HGFR). MET is normally expressed on epithelial cells, and improper activation can trigger tumor growth, angiogenesis and metastasis. DLL4 and cMET can be used as biomarkers to detect a vesicle population.

Biomarkers that can be derived and analyzed from a vesicle include miRNA (miR), miRNA*nonsense (miR*), and other RNAs (including, but not limited to, mRNA, preRNA, priRNA, hnRNA, snRNA, siRNA, shRNA). A miRNA biomarker can include not only its miRNA and microRNA* nonsense, but its precursor molecules: pri-microRNAs (pri-miRs) and pre-microRNAs (pre-miRs). The sequence of a miRNA can be obtained from publicly available databases such as http://www.mirbase.org/, http://www.microrna.org/, or any others available. Unless noted, the terms miR, miRNA and microRNA are used interchangeably throughout unless noted. In some embodiments, the methods of the invention comprise isolating vesicles, and assessing the miRNA payload within the isolated vesicles. The biomarker can also be a nucleic acid molecule (e.g. DNA), protein, or peptide. The presence or absence, expression level, mutations (for example genetic mutations, such as deletions, translocations, duplications, nucleotide or amino acid substitutions, and the like) can be determined for the biomarker. Any epigenetic modulation or copy number variation of a biomarker can also be analyzed.

The one or more biomarkers analyzed can be indicative of a particular tissue or cell of origin, disease, or physiological state. Furthermore, the presence, absence or expression level of one or more of the biomarkers described herein can be correlated to a phenotype of a subject, including a disease, condition, prognosis or drug efficacy. The specific biomarker and biosignature set forth below constitute non-inclusive examples for each of the diseases, condition comparisons, conditions, and/or physiological states. Furthermore, the one or more biomarker assessed for a phenotype can be a cell-of-origin specific vesicle.

The one or more miRNAs used to characterize a phenotype may be selected from those disclosed in PCT Publication No. WO/2009/036236. For example, one or more miRNAs listed in Tables I-VI (FIGS. 6-11) therein can be used to characterize colon adenocarcinoma, colorectal cancer, prostate cancer, lung cancer, breast cancer, b-cell lymphoma, pancreatic cancer, diffuse large BCL cancer, CLL, bladder cancer, renal cancer, hypoxia-tumor, uterine leiomyomas, ovarian cancer, hepatitis C virus-associated hepatocellular carcinoma, ALL, Alzheimer's disease, myelofibrosis, myelofibrosis, polycythemia vera, thrombocythemia, HIV, or HIV-I latency, as further described herein.

The one or more miRNAs can be detected in a vesicle. The one or more miRNAs can be miR-223, miR-484, miR-191, miR-146a, miR-016, miR-026a, miR-222, miR-024, miR-126, and miR-32. One or more miRNAs can also be detected in PBMC. The one or more miRNAs can be miR-223, miR-150, miR-146b, miR-016, miR-484, miR-146a, miR-191, miR-026a, miR-019b, or miR-020a. The one or more miRNAs can be used to characterize a particular disease or condition. For example, for the disease bladder cancer, one or more miRNAs can be detected, such as miR-223, miR-26b, miR-221, miR-103-1, miR-185, miR-23b, miR-203, miR-17-5p, miR-23a, miR-205 or any combination thereof. The one or more miRNAs may be upregulated or overexpressed in the disease setting.

In some embodiments, the one or more miRNAs is used to characterize hypoxia-tumor. The one or more miRNA may be miR-23, miR-24, miR-26, miR-27, miR-103, miR-107, miR-181, miR-210, or miR-213, and may be upregulated. One or more miRNAs can also be used to characterize uterine leiomyomas. For example, the one or more miRNAs used to characterize a uterine leiomyoma may be a let-7 family member, miR-21, miR-23b, miR-29b, or miR-197. The miRNA can be upregulated.

Myelofibrosis can also be characterized by one or more miRNAs, such as miR-190, which can be upregulated; miR-31, miR-150 and miR-95, which can be downregulated, or any combination thereof. Furthermore, myelofibrosis, polycythemia vera or thrombocythemia can also be characterized by detecting one or more miRNAs, such as, but not limited to, miR-34a, miR-342, miR-326, miR-105, miR-149, miR-147, or any combination thereof. The one or more miRNAs may be down-regulated.

Other examples of phenotypes that can be characterized by assessing a vesicle for one or more biomarkers are further described herein.

The one or more biomarkers can be detected using a probe. A probe can comprise an oligonucleotide, such as DNA or RNA, an aptamer, monoclonal antibody, polyclonal antibody, Fabs, Fab′, single chain antibody, synthetic antibody, peptoid, zDNA, peptide nucleic acid (PNA), locked nucleic acid (LNA), lectin, synthetic or naturally occurring chemical compound (including but not limited to a drug or labeling reagent), dendrimer, or a combination thereof. The probe can be directly detected, for example by being directly labeled, or be indirectly detected, such as through a labeling reagent. The probe can selectively recognize a biomarker. For example, a probe that is an oligonucleotide can selectively hybridize to a miRNA biomarker.

In aspects, the invention provides for the diagnosis, theranosis, prognosis, disease stratification, disease staging, treatment monitoring or predicting responder/non-responder status of a disease or disorder in a subject. The invention comprises assessing vesicles from a subject, including assessing biomarkers present on the vesicles and/or assessing payload within the vesicles, such as protein, nucleic acid or other biological molecules. Any appropriate biomarker that can be assessed using a vesicle and that relates to a disease or disorder can be used the carry out the methods of the invention. Furthermore, any appropriate technique to assess a vesicle as described herein can be used. Exemplary biomarkers are provided herein for illustrative purposes of using methods of the invention, and many of the same biomarkers are useful in methods of the invention for different diseases. Based on Applicants' discoveries and inventions herein, one of skill will appreciate that numerous other vesicle associated biomarkers can be used to create a biosignature for the diseases and disorders in addition to those specifically described here.

Any of the types of biomarkers or specific biomarkers described herein can be assessed as part of a biosignature. Exemplary biomarkers include without limitation those in Table 5. The markers in the table can be used for capture and/or detection of vesicles for characterizing phenotypes as disclosed herein. In some cases, multiple capture and/or detectors are used to enhance the characterization. The markers can be detected as protein or as mRNA, which can be circulating freely or in complex. The markers can be detected as vesicle surface antigens or and vesicle payload. The “Illustrative Class” indicates indications for which the markers are known markers. Those of skill will appreciate that the markers can also be used in alternate settings in certain instances. For example, a marker which can be used to characterize one type disease may also be used to characterize another disease as appropriate.

TABLE 5 Illustrative Vesicle Associated Biomarkers Illustrative Class Biomarkers Drug associated ABCC1, ABCG2, ACE2, ADA, ADH1C, ADH4, AGT, AR, AREG, ASNS, targets and BCL2, BCRP, BDCA1, beta III tubulin, BIRC5, B-RAF, BRCA1, BRCA2, CA2, prognostic markers caveolin, CD20, CD25, CD33, CD52, CDA, CDKN2A, CDKN1A, CDKN1B, CDK2, CDW52, CES2, CK14, CK17, CK5/6, c-KIT, c-Met, c-Myc, COX-2, Cyclin D1, DCK, DHFR, DNMT1, DNMT3A, DNMT3B, E-Cadherin, ECGF1, EGFR, EML4-ALK fusion, EPHA2, Epiregulin, ER, ERBR2, ERCC1, ERCC3, EREG, ESR1, FLT1, folate receptor, FOLR1, FOLR2, FSHB, FSHPRH1, FSHR, FYN, GART, GNRH1, GNRHR1, GSTP1, HCK, HDAC1, hENT-1, Her2/Neu, HGF, HIF1A, HIG1, HSP90, HSP90AA1, HSPCA, IGF-1R, IGFRBP, IGFRBP3, IGFRBP4, IGFRBP5, IL13RA1, IL2RA, KDR, Ki67, KIT, K-RAS, LCK, LTB, Lymphotoxin Beta Receptor, LYN, MET, MGMT, MLH1, MMR, MRP1, MS4A1, MSH2, MSH5, Myc, NFKB1, NFKB2, NFKBIA, NRAS, ODC1, OGFR, p16, p21, p27, p53, p95, PARP-1, PDGFC, PDGFR, PDGFRA, PDGFRB, PGP, PGR, PI3K, POLA, POLA1, PPARG, PPARGC1, PR, PTEN, PTGS2, PTPN12, RAF1, RARA, RRM1, RRM2, RRM2B, RXRB, RXRG, SIK2, SPARC, SRC, SSTR1, SSTR2, SSTR3, SSTR4, SSTR5, Survivin, TK1, TLE3, TNF, TOP1, TOP2A, TOP2B, TS, TUBB3, TXN, TXNRD1, TYMS, VDR, VEGF, VEGFA, VEGFC, VHL, YES1, ZAP70 Cancer treatment AR, AREG (Amphiregulin), BRAF, BRCA1, cKIT, cMET, EGFR, EGFR associated markers w/T790M, EML4-ALK, ER, ERBB3, ERBB4, ERCC1, EREG, GNA11, GNAQ, hENT-1, Her2, Her2 Exon 20 insert, IGF1R, Ki67, KRAS, MGMT, MGMT methylation, MSH2, MSI, NRAS, PGP (MDR1), PIK3CA, PR, PTEN, ROS1, ROS1 translocation, RRM1, SPARC, TLE3, TOPO1, TOPO2A, TS, TUBB3, VEGFR2 Cancer treatment AR, AREG, BRAF, BRCA1, cKIT, cMET, EGFR, EGFR w/T790M, EML4- associated markers ALK, ER, ERBB3, ERBB4, ERCC1, EREG, GNA11, GNAQ, Her2, Her2 Exon 20 insert, IGFR1, Ki67, KRAS, MGMT-Me, MSH2, MSI, NRAS, PGP (MDR-1), PIK3CA, PR, PTEN, ROS1 translocation, RRM1, SPARC, TLE3, TOPO1, TOPO2A, TS, TUBB3, VEGFR2 Colon cancer AREG, BRAF, EGFR, EML4-ALK, ERCC1, EREG, KRAS, MSI, NRAS, treatment associated PIK3CA, PTEN, TS, VEGFR2 markers Colon cancer AREG, BRAF, EGFR, EML4-ALK, ERCC1, EREG, KRAS, MSI, NRAS, treatment associated PIK3CA, PTEN, TS, VEGFR2 markers Melanoma treatment BRAF, cKIT, ERBB3, ERBB4, ERCC1, GNA11, GNAQ, MGMT, MGMT associated markers methylation, NRAS, PIK3CA, TUBB3, VEGFR2 Melanoma treatment BRAF, cKIT, ERBB3, ERBB4, ERCC1, GNA11, GNAQ, MGMT-Me, NRAS, associated markers PIK3CA, TUBB3, VEGFR2 Ovarian cancer BRCA1, cMET, EML4-ALK, ER, ERBB3, ERCC1, hENT-1, HER2, IGF1R, treatment associated PGP(MDR1), PIK3CA, PR, PTEN, RRM1, TLE3, TOPO1, TOPO2A, TS markers Ovarian cancer BRCA1, cMET, EML4-ALK (translocation), ER, ERBB3, ERCC1, HER2, treatment associated PIK3CA, PR, PTEN, RRM1, TLE3, TS markers Breast cancer BRAF, BRCA1, EGFR, EGFR T790M, EML4-ALK, ER, ERBB3, ERCC1, treatment associated HER2, Ki67, PGP (MDR1), PIK3CA, PR, PTEN, ROS1, ROS1 translocation, markers RRM1, TLE3, TOPO1, TOPO2A, TS Breast cancer BRAF, BRCA1, EGFR w/T790M, EML4-ALK, ER, ERBB3, ERCC1, HER2, treatment associated Ki67, KRAS, PIK3CA, PR, PTEN, ROS1 translocation, RRM1, TLE3, TOPO1, markers TOPO2A, TS NSCLC cancer BRAF, BRCA1, cMET, EGFR, EGFR w/T790M, EML4-ALK, ERCC1, Her2 treatment associated Exon 20 insert, KRAS, MSH2, PIK3CA, PTEN, ROS1 (trans), RRM1, TLE3, TS, markers VEGFR2 NSCLC cancer BRAF, cMET, EGFR, EGFR w/T790M, EML4-ALK, ERCC1, Her2 Exon 20 treatment associated insert, KRAS, MSH2, PIK3CA, PTEN, ROS1 translocation, RRM1, TLE3, TS markers Cancer/Angio Erb 2, Erb 3, Erb 4, UNC93a, B7H3, MUC1, MUC2, MUC16, MUC17, 5T4, RAGE, VEGFA, VEGFR2, FLT1, DLL4, Epcam Tissue (Breast) BIG H3, GCDFP-15, PR(B), GPR30, CYFRA21, BRCA1, BRCA2, ESR1, ESR2 Tissue (Prostate) PSMA, PCSA, PSCA, PSA, TMPRSS2 Inflammation/Immune MFG-E8, IFNAR, CD40, CD80, MICB, HLA-DRb, IL-17-Ra Common vesicle HSPA8, CD63, Actb, GAPDH, CD9, CD81, ANXA2, HSP90AA1, ENO1, markers YWHAZ, PDCD6IP, CFL1, SDCBP, PKN2, MSN, MFGE8, EZR, YWHAG, PGK1, EEF1A1, PPIA, GLC1F, GK, ANXA6, ANXA1, ALDOA, ACTG1, TPI1, LAMP2, HSP90AB1, DPP4, YWHAB, TSG101, PFN1, LDHB, HSPA1B, HSPA1A, GSTP1, GNAI2, GDI2, CLTC, ANXA5, YWHAQ, TUBA1A, THBS1, PRDX1, LDHA, LAMP1, CLU, CD86 Common vesicle CD63, GAPDH, CD9, CD81, ANXA2, ENO1, SDCBP, MSN, MFGE8, EZR, membrane markers GK, ANXA1, LAMP2, DPP4, TSG101, HSPA1A, GDI2, CLTC, LAMP1, CD86, ANPEP, TFRC, SLC3A2, RDX, RAP1B, RAB5C, RAB5B, MYH9, ICAM1, FN1, RAB11B, PIGR, LGALS3, ITGB1, EHD1, CLIC1, ATP1A1, ARF1, RAP1A, P4HB, MUC1, KRT10, HLA-A, FLOT1, CD59, C1orf58, BASP1, TACSTD1, STOM Common vesicle MHC class I, MHC class II, Integrins, Alpha 4 beta 1, Alpha M beta 2, Beta 2, markers ICAM1/CD54, P-selection, Dipeptidylpeptidase IV/CD26, Aminopeptidase n/CD13, CD151, CD53, CD37, CD82, CD81, CD9, CD63, Hsp70, Hsp84/90 Actin, Actin-binding proteins, Tubulin, Annexin I, Annexin II, Annexin IV, Annexin V, Annexin VI, RAB7/RAP1B/RADGDI, Gi2alpha/14-3-3, CBL/LCK, CD63, GAPDH, CD9, CD81, ANXA2, ENO1, SDCBP, MSN, MFGE8, EZR, GK, ANXA1, LAMP2, DPP4, TSG101, HSPA1A, GDI2, CLTC, LAMP1, Cd86, ANPEP, TFRC, SLC3A2, RDX, RAP1B, RAB5C, RAB5B, MYH9, ICAM1, FN1, RAB11B, PIGR, LGALS3, ITGB1, EHD1, CLIC1, ATP1A1, ARF1, RAP1A, P4HB, MUC1, KRT10, HLA-A, FLOT1, CD59, C1orf58, BASP1, TACSTD1, STOM Vesicle markers A33, a33 n15, AFP, ALA, ALIX, ALP, AnnexinV, APC, ASCA, ASPH (246-260), ASPH (666-680), ASPH (A-10), ASPH (D01P), ASPH (D03), ASPH (G- 20), ASPH (H-300), AURKA, AURKB, B7H3, B7H4, BCA-225, BCNP, BDNF, BRCA, CA125 (MUC16), CA-19-9, C-Bir, CD1.1, CD10, CD174 (Lewis y), CD24, CD44, CD46, CD59 (MEM-43), CD63, CD66e CEA, CD73, CD81, CD9, CDA, CDAC11a2, CEA, C-Erb2, C-erbB2, CRMP-2, CRP, CXCL12, CYFRA21-1, DLL4, DR3, EGFR, Epcam, EphA2, EphA2 (H-77), ER, ErbB4, EZH2, FASL, FRT, FRT c.f23, GDF15, GPCR, GPR30, Gro-alpha, HAP, HBD1, HBD2, HER3 (ErbB3), HSP, HSP70, hVEGFR2, iC3b, IL6 Unc, IL-1B, IL6 Unc, IL6R, IL8, IL-8, INSIG-2, KLK2, L1CAM, LAMN, LDH, MACC-1, MAPK4, MART-1, MCP-1, M-CSF, MFG-E8, MIC1, MIF, MIS RII, MMG, MMP26, MMP7, MMP9, MS4A1, MUC1, MUC1 seq1, MUC1 seq11A, MUC17, MUC2, Ncam, NGAL, NPGP/NPFF2, OPG, OPN, p53, p53, PA2G4, PBP, PCSA, PDGFRB, PGP9.5, PIM1, PR (B), PRL, PSA, PSMA, PSME3, PTEN, R5- CD9 Tube 1, Reg IV, RUNX2, SCRN1, seprase, SERPINB3, SPARC, SPB, SPDEF, SRVN, STAT3, STEAP1, TF (FL-295), TFF3, TGM2, TIMP-1, TIMP1, TIMP2, TMEM211, TMPRSS2, TNF-alpha, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK, UNC93A, VEGFA, YPSMA-1 Vesicle markers NSE, TRIM29, CD63, CD151, ASPH, LAMP2, TSPAN1, SNAIL, CD45, CKS1, NSE, FSHR, OPN, FTH1, PGP9, ANNEXIN1, SPD, CD81, EPCAM, PTH1R, CEA, CYTO7, CCL2, SPA, KRAS, TWIST1, AURKB, MMP9, P27, MMP1, HLA, HIF, CEACAM, CENPH, BTUB, INTGb4, EGFR, NACC1, CYTO18, NAP2, CYTO19, ANNEXINV, TGM2, ERB2, BRCA1, B7H3, SFTPC, PNT, NCAM, MS4A1, P53, INGA3, MUC2, SPA, OPN, CD63, CD9, MUC1, UNCR3, PAN ADH, HCG, TIMP, PSMA, GPCR, RACK1, PSCA, VEGF, BMP2, CD81, CRP, PROGRP, B7H3, MUC1, M2PK, CD9, PCSA, PSMA Vesicle markers TFF3, MS4A1, EphA2, GAL3, EGFR, N-gal, PCSA, CD63, MUC1, TGM2, CD81, DR3, MACC-1, TrKB, CD24, TIMP-1, A33, CD66 CEA, PRL, MMP9, MMP7, TMEM211, SCRN1, TROP2, TWEAK, CDACC1, UNC93A, APC, C- Erb, CD10, BDNF, FRT, GPR30, P53, SPR, OPN, MUC2, GRO-1, tsg 101, GDF15 Vesicle markers CD9, Erb2, Erb4, CD81, Erb3, MUC16, CD63, DLL4, HLA-Drpe, B7H3, IFNAR, 5T4, PCSA, MICB, PSMA, MFG-E8, Muc1, PSA, Muc2, Unc93a, VEGFR2, EpCAM, VEGFA, TMPRSS2, RAGE, PSCA, CD40, Muc17, IL-17- RA, CD80 Benign Prostate BCMA, CEACAM-1, HVEM, IL-1R4, IL-10Rb, Trappin-2, p53, hsa-miR-329, Hyperplasia (BPH) hsa-miR-30a, hsa-miR-335, hsa-miR-152, hsa-miR-151-5p, hsa-miR-200a, hsa- miR-145, hsa-miR-29a, hsa-miR-106b, hsa-miR-595, hsa-miR-142-5p, hsa-miR- 99a, hsa-miR-20b, hsa-miR-373, hsa-miR-502-5p, hsa-miR-29b, hsa-miR-142-3p, hsa-miR-663, hsa-miR-423-5p, hsa-miR-15a, hsa-miR-888, hsa-miR-361-3p, hsa- miR-365, hsa-miR-10b, hsa-miR-199a-3p, hsa-miR-181a, hsa-miR-19a, hsa-miR- 125b, hsa-miR-760, hsa-miR-7a, hsa-miR-671-5p, hsa-miR-7c, hsa-miR-1979, hsa-miR-103 Metastatic Prostate hsa-miR-100, hsa-miR-1236, hsa-miR-1296, hsa-miR-141, hsa-miR-146b-5p, hsa- Cancer miR-17*, hsa-miR-181a, hsa-miR-200b, hsa-miR-20a*, hsa-miR-23a*, hsa-miR- 331-3p, hsa-miR-375, hsa-miR-452, hsa-miR-572, hsa-miR-574-3p, hsa-miR-577, hsa-miR-582-3p, hsa-miR-937, miR-10a, miR-134, miR-141, miR-200b, miR-30a, miR-32, miR-375, miR-495, miR-564, miR-570, miR-574-3p, miR-885-3p Metastatic Prostate hsa-miR-200b, hsa-miR-375, hsa-miR-141, hsa-miR-331-3p, hsa-miR-181a, hsa- Cancer miR-574-3p Metastatic Prostate FOX01A, SOX9, CLNS1A, PTGDS, XPO1, LETMD1, RAD23B, ABCC3, APC, Cancer CHES1, EDNRA, FRZB, HSPG2, TMPRSS2_ETV1 fusion Prostate Cancer hsa-let-7b, hsa-miR-107, hsa-miR-1205, hsa-miR-1270, hsa-miR-130b, hsa-miR- 141, hsa-miR-143, hsa-miR-148b*, hsa-miR-150, hsa-miR-154*, hsa-miR-181a*, hsa-miR-181a-2*, hsa-miR-18a*, hsa-miR-19b-1*, hsa-miR-204, hsa-miR-2110, hsa-miR-215, hsa-miR-217, hsa-miR-219-2-3p, hsa-miR-23b*, hsa-miR-299-5p, hsa-miR-301a, hsa-miR-301a, hsa-miR-326, hsa-miR-331-3p, hsa-miR-365*, hsa- miR-373*, hsa-miR-424, hsa-miR-424*, hsa-miR-432, hsa-miR-450a, hsa-miR- 451, hsa-miR-484, hsa-miR-497, hsa-miR-517*, hsa-miR-517a, hsa-miR-518f, hsa-miR-574-3p, hsa-miR-595, hsa-miR-617, hsa-miR-625*, hsa-miR-628-5p, hsa-miR-629, hsa-miR-634, hsa-miR-769-5p, hsa-miR-93, hsa-miR-96 Prostate Cancer CD9, PSMA, PCSA, CD63, CD81, B7H3, IL6, OPG-13, IL6R, PA2G4, EZH2, RUNX2, SERPINB3, EpCam Prostate Cancer A33, a33 n15, AFP, ALA, ALIX, ALP, AnnexinV, APC, ASCA, ASPH (246-260), ASPH (666-680), ASPH (A-10), ASPH (D01P), ASPH (D03), ASPH (G- 20), ASPH (H-300), AURKA, AURKB, B7H3, B7H4, BCA-225, BCNP, BDNF, BRCA, CA125 (MUC16), CA-19-9, C-Bir, CD1.1, CD10, CD174 (Lewis y), CD24, CD44, CD46, CD59 (MEM-43), CD63, CD66e CEA, CD73, CD81, CD9, CDA, CDAC11a2, CEA, C-Erb2, C-erbB2, CRMP-2, CRP, CXCL12, CYFRA21-1, DLL4, DR3, EGFR, Epcam, EphA2, EphA2 (H-77), ER, ErbB4, EZH2, FASL, FRT, FRT c.f23, GDF15, GPCR, GPR30, Gro-alpha, HAP, HBD1, HBD2, HER3 (ErbB3), HSP, HSP70, hVEGFR2, iC3b, IL6 Unc, IL-1B, IL6 Unc, IL6R, IL8, IL-8, INSIG-2, KLK2, L1CAM, LAMN, LDH, MACC-1, MAPK4, MART-1, MCP-1, M-CSF, MFG-E8, MIC1, MIF, MIS RII, MMG, MMP26, MMP7, MMP9, MS4A1, MUC1, MUC1 seq1, MUC1 seq11A, MUC17, MUC2, Ncam, NGAL, NPGP/NPFF2, OPG, OPN, p53, p53, PA2G4, PBP, PCSA, PDGFRB, PGP9.5, PIM1, PR (B), PRL, PSA, PSMA, PSME3, PTEN, R5- CD9 Tube 1, Reg IV, RUNX2, SCRN1, seprase, SERPINB3, SPARC, SPB, SPDEF, SRVN, STAT3, STEAP1, TF (FL-295), TFF3, TGM2, TIMP-1, TIMP1, TIMP2, TMEM211, TMPRSS2, TNF-alpha, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK, UNC93A, VEGFA, YPSMA-1 Prostate Cancer 5T4, ACTG1, ADAM10, ADAM15, ALDOA, ANXA2, ANXA6, APOA1, Vesicle Markers ATP1A1, BASP1, C1orf58, C20orf114, C8B, CAPZA1, CAV1, CD151, CD2AP, CD59, CD9, CD9, CFL1, CFP, CHMP4B, CLTC, COTL1, CTNND1, CTSB, CTSZ, CYCS, DPP4, EEF1A1, EHD1, ENO1, F11R, F2, F5, FAM125A, FNBP1L, FOLH1, GAPDH, GLB1, GPX3, HIST1H1C, HIST1H2AB, HSP90AB1, HSPA1B, HSPA8, IGSF8, ITGB1, ITIH3, JUP, LDHA, LDHB, LUM, LYZ, MFGE8, MGAM, MMP9, MYH2, MYL6B, NME1, NME2, PABPC1, PABPC4, PACSIN2, PCBP2, PDCD6IP, PRDX2, PSA, PSMA, PSMA1, PSMA2, PSMA4, PSMA6, PSMA7, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB8, PTGFRN, RPS27A, SDCBP, SERINC5, SH3GL1, SLC3A2, SMPDL3B, SNX9, TACSTD1, TCN2, THBS1, TPI1, TSG101, TUBB, VDAC2, VPS37B, YWHAG, YWHAQ, YWHAZ Prostate Cancer FLNA, DCRN, HER3 (ErbB3), VCAN, CD9, GAL3, CDADC1, GM-CSF, Vesicle Markers EGFR, RANK, CSA, PSMA, ChickenIgY, B7H3, PCSA, CD63, CD3, MUC1, TGM2, CD81, S100-A4, MFG-E8, Integrin, NK-2R(C-21), PSA, CD24, TIMP-1, IL6 Unc, PBP, PIM1, CA-19-9, Trail-R4, MMP9, PRL, EphA2, TWEAK, NY- ESO-1, Mammaglobin, UNC93A, A33, AURKB, CD41, XAGE-1, SPDEF, AMACR, seprase/FAP, NGAL, CXCL12, FRT, CD66e CEA, SIM2 (C-15), C- Bir, STEAP, PSIP1/LEDGF, MUC17, hVEGFR2, ERG, MUC2, ADAM10, ASPH (A-10), CA125, Gro-alpha, Tsg 101, SSX2, Trail-R4 Prostate Cancer NT5E (CD73), A33, ABL2, ADAM10, AFP, ALA, ALIX, ALPL, AMACR, Apo Vesicle Markers J/CLU, ASCA, ASPH (A-10), ASPH (D01P), AURKB, B7H3, B7H4, BCNP, BDNF, CA125 (MUC16), CA-19-9, C-Bir (Flagellin), CD10, CD151, CD24, CD3, CD41, CD44, CD46, CD59(MEM-43), CD63, CD66e CEA, CD81, CD9, CDA, CDADC1, C-erbB2, CRMP-2, CRP, CSA, CXCL12, CXCR3, CYFRA21- 1, DCRN, DDX-1, DLL4, EGFR, EpCAM, EphA2, ERG, EZH2, FASL, FLNA, FRT, GAL3, GATA2, GM-CSF, Gro-alpha, HAP, HER3 (ErbB3), HSP70, HSPB1, hVEGFR2, iC3b, IL-1B, IL6R, IL6 Unc, IL7R alpha/CD127, IL8, INSIG-2, Integrin, KLK2, Label, LAMN, Mammaglobin, M-CSF, MFG-E8, MIF, MIS RII, MMP7, MMP9, MS4A1, MUC1, MUC17, MUC2, Ncam, NDUFB7, NGAL, NK-2R(C-21), NY-ESO-1, p53, PBP, PCSA, PDGFRB, PIM1, PRL, PSA, PSIP1/LEDGF, PSMA, RAGE, RANK, Reg IV, RUNX2, S100-A4, seprase/FAP, SERPINB3, SIM2 (C-15), SPARC, SPC, SPDEF, SPP1, SSX2, SSX4, STEAP, STEAP4, TFF3, TGM2, TIMP-1, TMEM211, Trail-R2, Trail-R4, TrKB (poly), Trop2, Tsg 101, TWEAK, UNC93A, VCAN, VEGFA, wnt-5a(C- 16), XAGE, XAGE-1 Prostate Cancer hsa-miR-1974, hsa-miR-27b, hsa-miR-103, hsa-miR-146a, hsa-miR-22, hsa-miR- Treatment 382, hsa-miR-23a, hsa-miR-376c, hsa-miR-335, hsa-miR-142-5p, hsa-miR-221, hsa-miR-142-3p, hsa-miR-151-3p, hsa-miR-21, hsa-miR-16 Prostate Cancer let-7d, miR-148a, miR-195, miR-25, miR-26b, miR-329, miR-376c, miR-574-3p, miR-888, miR-9, miR1204, miR-16-2*, miR-497, miR-588, miR-614, miR-765, miR92b*, miR-938, let-7f-2*, miR-300, miR-523, miR-525-5p, miR-1182, miR- 1244, miR-520d-3p, miR-379, let-7b, miR-125a-3p, miR-1296, miR-134, miR- 149, miR-150, miR-187, miR-32, miR-324-3p, miR-324-5p, miR-342-3p, miR- 378, miR-378*, miR-384, miR-451, miR-455-3p, miR-485-3p, miR-487a, miR- 490-3p, miR-502-5p, miR-548a-5p, miR-550, miR-562, miR-593, miR-593*, miR-595, miR-602, miR-603, miR-654-5p, miR-877*, miR-886-5p, miR-125a-5p, miR-140-3p, miR-192, miR-196a, miR-2110, miR-212, miR-222, miR-224*, miR-30b*, miR-499-3p, miR-505* Prostate Cancer hsa-miR-451, hsa-miR-223, hsa-miR-593*, hsa-miR-1974, hsa-miR-486-5p, hsa- miR-19b, hsa-miR-320b, hsa-miR-92a, hsa-miR-21, hsa-miR-675*, hsa-miR-16, hsa-miR-876-5p, hsa-miR-144, hsa-miR-126, hsa-miR-137, hsa-miR-1913, hsa- miR-29b-1*, hsa-miR-15a, hsa-miR-93, hsa-miR-1266 Prostate Cancer miR-148a, miR-329, miR-9, miR-378*, miR-25, miR-614, miR-518c*, miR-378, miR-765, let-7f-2*, miR-574-3p, miR-497, miR-32, miR-379, miR-520g, miR- 542-5p, miR-342-3p, miR-1206, miR-663, miR-222 Prostate Cancer hsa-miR-877*, hsa-miR-593, hsa-miR-595, hsa-miR-300, hsa-miR-324-5p, hsa- miR-548a-5p, hsa-miR-329, hsa-miR-550, hsa-miR-886-5p, hsa-miR-603, hsa- miR-490-3p, hsa-miR-938, hsa-miR-149, hsa-miR-150, hsa-miR-1296, hsa-miR- 384, hsa-miR-487a, hsa-miRPlus-C1089, hsa-miR-485-3p, hsa-miR-525-5p Prostate Cancer miR-588, miR-1258, miR-16-2*, miR-938, miR-526b, miR-92b*, let-7d, miR- 378*, miR-124, miR-376c, miR-26b, miR-1204, miR-574-3p, miR-195, miR-499- 3p, miR-2110, miR-888 Prostate Cancer miR-183-96-182 cluster (miRs-183, 96 and 182), metal ion transporter such as hZIP1, SLC39A1, SLC39A2, SLC39A3, SLC39A4, SLC39A5, SLC39A6, SLC39A7, SLC39A8, SLC39A9, SLC39A10, SLC39A11, SLC39A12, SLC39A13, SLC39A14 Prostate Cancer RAD23B, FBP1, TNFRSF1A, CCNG2, NOTCH3, ETV1, BID, SIM2, LETMD1, ANXA1, miR-519d, and miR-647 Prostate Cancer RAD23B, FBP1, TNFRSF1A, NOTCH3, ETV1, BID, SIM2, ANXA1 and BCL2 Prostate Cancer ANPEP, ABL1, PSCA, EFNA1, HSPB1, INMT and TRIP13 Prostate Cancer E2F3, c-met, pRB, EZH2, e-cad, CAXII, CAIX, HIF-1α, Jagged, PIM-1, hepsin, RECK, Clusterin, MMP9, MTSP-1, MMP24, MMP15, IGFBP-2, IGFBP-3, E2F4, caveolin, EF-1A, Kallikrein 2, Kallikrein 3, PSGR Colorectal cancer CD9, EGFR, NGAL, CD81, STEAP, CD24, A33, CD66E, EPHA2, Ferritin, GPR30, GPR110, MMP9, OPN, p53, TMEM211, TROP2, TGM2, TIMP, EGFR, DR3, UNC93A, MUC17, EpCAM, MUC1, MUC2, TSG101, CD63, B7H3 Colorectal cancer DR3, STEAP, epha2, TMEM211, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, TETS Colorectal cancer A33, AFP, ALIX, ALX4, ANCA, APC, ASCA, AURKA, AURKB, B7H3, BANK1, BCNP, BDNF, CA-19-9, CCSA-2, CCSA-3&4, CD10, CD24, CD44, CD63, CD66 CEA, CD66e CEA, CD81, CD9, CDA, C-Erb2, CRMP-2, CRP, CRTN, CXCL12, CYFRA21-1, DcR3, DLL4, DR3, EGFR, Epcam, EphA2, FASL, FRT, GAL3, GDF15, GPCR (GPR110), GPR30, GRO-1, HBD1, HBD2, HNP1-3, IL-1B, IL8, IMP3, L1CAM, LAMN, MACC-1, MGC20553, MCP-1, M- CSF, MIC1, MIF, MMPI, MMP9, MS4A1, MUC1, MUC17, MUC2, Ncam, NGAL, NNMT, OPN, p53, PCSA, PDGFRB, PRL, PSMA, PSME3, Reg IV, SCRN1, Sept-9, SPARC, SPON2, SPR, SRVN, TFF3, TGM2, TIMP-1, TMEM211, TNF-alpha, TPA, TPS, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK, UNC93A, VEGFA Colorectal cancer miR 92, miR 21, miR 9, miR 491 Colorectal cancer miR-127-3p, miR-92a, miR-486-3p, miR-378 Colorectal cancer TMEM211, MUC1, CD24 and/or GPR110 (GPCR110) Colorectal cancer hsa-miR-376c, hsa-miR-215, hsa-miR-652, hsa-miR-582-5p, hsa-miR-324-5p, hsa-miR-1296, hsa-miR-28-5p, hsa-miR-190, hsa-miR-590-5p, hsa-miR-202, hsa- miR-195 Colorectal cancer A26C1A, A26C1B, A2M, ACAA2, ACE, ACOT7, ACP1, ACTA1, ACTA2, vesicle markers ACTB, ACTBL2, ACTBL3, ACTC1, ACTG1, ACTG2, ACTN1, ACTN2, ACTN4, ACTR3, ADAM10, ADSL, AGR2, AGR3, AGRN, AHCY, AHNAK, AKR1B10, ALB, ALDH16A1, ALDH1A1, ALDOA, ANXA1, ANXA11, ANXA2, ANXA2P2, ANXA4, ANXA5, ANXA6, AP2A1, AP2A2, APOA1, ARF1, ARF3, ARF4, ARF5, ARF6, ARHGDIA, ARPC3, ARPC5L, ARRDC1, ARVCF, ASCC3L1, ASNS, ATP1A1, ATP1A2, ATP1A3, ATP1B1, ATP4A, ATP5A1, ATP5B, ATP5I, ATP5L, ATP5O, ATP6AP2, B2M, BAIAP2, BAIAP2L1, BRI3BP, BSG, BUB3, C1orf58, C5orf32, CAD, CALM1, CALM2, CALM3, CAND1, CANX, CAPZA1, CBR1, CBR3, CCT2, CCT3, CCT4, CCT5, CCT6A, CCT7, CCT8, CD44, CD46, CD55, CD59, CD63, CD81, CD82, CD9, CDC42, CDH1, CDH17, CEACAM5, CFL1, CFL2, CHMP1A, CHMP2A, CHMP4B, CKB, CLDN3, CLDN4, CLDN7, CLIC1, CLIC4, CLSTN1, CLTC, CLTCL1, CLU, COL12A1, COPB1, COPB2, CORO1C, COX4I1, COX5B, CRYZ, CSPG4, CSRP1, CST3, CTNNA1, CTNNB1, CTNND1, CTTN, CYFIP1, DCD, DERA, DIP2A, DIP2B, DIP2C, DMBT1, DPEP1, DPP4, DYNC1H1, EDIL3, EEF1A1, EEF1A2, EEF1AL3, EEF1G, EEF2, EFNB1, EGFR, EHD1, EHD4, EIF3EIP, EIF3I, EIF4A1, EIF4A2, ENO1, ENO2, ENO3, EPHA2, EPHA5, EPHB1, EPHB2, EPHB3, EPHB4, EPPK1, ESD, EZR, F11R, F5, F7, FAM125A, FAM125B, FAM129B, FASLG, FASN, FAT, FCGBP, FER1L3, FKBP1A, FLNA, FLNB, FLOT1, FLOT2, G6PD, GAPDH, GARS, GCN1L1, GDI2, GK, GMDS, GNA13, GNAI2, GNAI3, GNAS, GNB1, GNB2, GNB2L1, GNB3, GNB4, GNG12, GOLGA7, GPA33, GPI, GPRC5A, GSN, GSTP1, H2AFJ, HADHA, hCG_1757335, HEPH, HIST1H2AB, HIST1H2AE, HIST1H2AJ, HIST1H2AK, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E, HIST1H4F, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K, HIST1H4L, HIST2H2AC, HIST2H4A, HIST2H4B, HIST3H2A, HIST4H4, HLA- A, HLA-A29.1, HLA-B, HLA-C, HLA-E, HLA-H, HNRNPA2B1, HNRNPH2, HPCAL1, HRAS, HSD17B4, HSP90AA1, HSP90AA2, HSP90AA4P, HSP90AB1, HSP90AB2P, HSP90AB3P, HSP90B1, HSPA1A, HSPA1B, HSPA1L, HSPA2, HSPA4, HSPA5, HSPA6, HSPA7, HSPA8, HSPA9, HSPD1, HSPE1, HSPG2, HYOU1, IDH1, IFITM1, IFITM2, IFITM3, IGH@, IGHG1, IGHG2, IGHG3, IGHG4, IGHM, IGHV4-31, IGK@, IGKC, IGKV1-5, IGKV2- 24, IGKV3-20, IGSF3, IGSF8, IQGAP1, IQGAP2, ITGA2, ITGA3, ITGA6, ITGAV, ITGB1, ITGB4, JUP, KIAA0174, KIAA1199, KPNB1, KRAS, KRT1, KRT10, KRT13, KRT14, KRT15, KRT16, KRT17, KRT18, KRT19, KRT2, KRT20, KRT24, KRT25, KRT27, KRT28, KRT3, KRT4, KRT5, KRT6A, KRT6B, KRT6C, KRT7, KRT75, KRT76, KRT77, KRT79, KRT8, KRT9, LAMA5, LAMP1, LDHA, LDHB, LFNG, LGALS3, LGALS3BP, LGALS4, LIMA1, LIN7A, LIN7C, LOC100128936, LOC100130553, LOC100133382, LOC100133739, LOC284889, LOC388524, LOC388720, LOC442497, LOC653269, LRP4, LRPPRC, LRSAM1, LSR, LYZ, MAN1A1, MAP4K4, MARCKS, MARCKSL1, METRNL, MFGE8, MICA, MIF, MINK1, MITD1, MMP7, MOBKL1A, MSN, MTCH2, MUC13, MYADM, MYH10, MYH11, MYH14, MYH9, MYL6, MYL6B, MYO1C, MYO1D, NARS, NCALD, NCSTN, NEDD4, NEDD4L, NME1, NME2, NOTCH1, NQO1, NRAS, P4HB, PCBP1, PCNA, PCSK9, PDCD6, PDCD6IP, PDIA3, PDXK, PEBP1, PFN1, PGK1, PHB, PHB2, PKM2, PLEC1, PLEKHB2, PLSCR3, PLXNA1, PLXNB2, PPIA, PPIB, PPP2R1A, PRDX1, PRDX2, PRDX3, PRDX5, PRDX6, PRKAR2A, PRKDC, PRSS23, PSMA2, PSMC6, PSMD11, PSMD3, PSME3, PTGFRN, PTPRF, PYGB, QPCT, QSOX1, RAB10, RAB11A, RAB11B, RAB13, RAB14, RAB15, RAB1A, RAB1B, RAB2A, RAB33B, RAB35, RAB43, RAB4B, RAB5A, RAB5B, RAB5C, RAB6A, RAB6B, RAB7A, RAB8A, RAB8B, RAC1, RAC3, RALA, RALB, RAN, RANP1, RAP1A, RAP1B, RAP2A, RAP2B, RAP2C, RDX, REG4, RHOA, RHOC, RHOG, ROCK2, RP11-631M21.2, RPL10A, RPL12, RPL6, RPL8, RPLP0, RPLP0-like, RPLP1, RPLP2, RPN1, RPS13, RPS14, RPS15A, RPS16, RPS18, RPS20, RPS21, RPS27A, RPS3, RPS4X, RPS4Y1, RPS4Y2, RPS7, RPS8, RPSA, RPSAP15, RRAS, RRAS2, RUVBL1, RUVBL2, S100A10, S100A11, S100A14, S100A16, S100A6, S100P, SDC1, SDC4, SDCBP, SDCBP2, SERINC1, SERINC5, SERPINA1, SERPINF1, SETD4, SFN, SLC12A2, SLC12A7, SLC16A1, SLC1A5, SLC25A4, SLC25A5, SLC25A6, SLC29A1, SLC2A1, SLC3A2, SLC44A1, SLC7A5, SLC9A3R1, SMPDL3B, SNAP23, SND1, SOD1, SORT1, SPTAN1, SPTBN1, SSBP1, SSR4, TACSTD1, TAGLN2, TBCA, TCEB1, TCP1, TF, TFRC, THBS1, TJP2, TKT, TMED2, TNFSF10, TNIK, TNKS1BP1, TNPO3, TOLLIP, TOMM22, TPI1, TPM1, TRAP1, TSG101, TSPAN1, TSPAN14, TSPAN15, TSPAN6, TSPAN8, TSTA3, TTYH3, TUBA1A, TUBA1B, TUBA1C, TUBA3C, TUBA3D, TUBA3E, TUBA4A, TUBA4B, TUBA8, TUBB, TUBB2A, TUBB2B, TUBB2C, TUBB3, TUBB4, TUBB4Q, TUBB6, TUFM, TXN, UBA1, UBA52, UBB, UBC, UBE2N, UBE2V2, UGDH, UQCRC2, VAMP1, VAMP3, VAMP8, VCP, VIL1, VPS25, VPS28, VPS35, VPS36, VPS37B, VPS37C, WDR1, YWHAB, YWHAE, YWHAG, YWHAH, YWHAQ, YWHAZ Colorectal Cancer hsa-miR-16, hsa-miR-25, hsa-miR-125b, hsa-miR-451, hsa-miR-200c, hsa-miR- 140-3p, hsa-miR-658, hsa-miR-370, hsa-miR-1296, hsa-miR-636, hsa-miR-502- 5p Prostate Cancer NY-ESO-1, SSX-2, SSX-4, XAGE-lb, AMACR, p90 autoantigen, LEDGF Breast cancer miR-21, miR-155, miR-206, miR-122a, miR-210, miR-21, miR-155, miR-206, miR-122a, miR-210, let-7, miR-10b, miR-125a, miR-125b, miR-145, miR-143, miR-145, miR-1b Breast cancer GAS5 Breast cancer ER, PR, HER2, MUC1, EGFR, KRAS, B-Raf, CYP2D6, hsp70, MART-1, TRP, HER2, hsp70, MART-1, TRP, HER2, ER, PR, Class III b-tubulin, VEGFA, ETV6-NTRK3, BCA-225, hsp70, MART1, ER, VEGFA, Class III b-tubulin, HER2/neu (e.g., for Her2+ breast cancer), GPR30, ErbB4 (JM) isoform, MPR8, MISIIR, CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK- 1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, progesterone receptor (PR) or its isoform (PR(A) or PR(B)), P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTPIA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2, Elafin, ST2/IL1R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR Breast cancer CD9, MIS Rii, ER, CD63, MUC1, HER3, STAT3, VEGFA, BCA, CA125, CD24, EPCAM, ERBB4 Breast cancer CD10, NPGP/NPFF2, HER2/ERBB2, AGTR1, NPY1R, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1 Breast cancer SPB, SPC, NSE, PGP9.5, CD9, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, EGFR, B7H3, IC3b, MUC1, mesothelin, SPA, PCSA, CD63, STEAP, AQP5, CD81, DR3, PSM, GPCR, EphA2, hCEA-CAM, PTPIA-2, CABYR, TMEM211, ADAM28, UNC93A, A33, CD24, CD10, NGAL, EpCam, MUC17, TROP-2, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR Breast cancer BRCA, MUC-1, MUC 16, CD24, ErbB4, ErbB2 (HER2), ErbB3, HSP70, Mammaglobin, PR, PR(B), VEGFA Breast cancer CD9, HSP70, Gal3, MIS, EGFR, ER, ICB3, CD63, B7H4, MUC1, DLL4, CD81, ERB3, VEGF, BCA225, BRCA, CA125, CD174, CD24, ERB2, NGAL, GPR30, CYFRA21, CD31, cMET, MUC2, ERBB4 Breast cancer CD9, EphA2, EGFR, B7H3, PSMA, PCSA, CD63, STEAP, CD81, STEAP1, ICAM1 (CD54), PSMA, A33, DR3, CD66e, MFG-8e, TMEM211, TROP-2, EGFR, Mammoglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, NK-2, EpCam, NK-1R, PSMA, 5T4, PAI-1, CD45 Breast cancer PGP9.5, CD9, HSP70, gal3-b2c10, EGFR, iC3b, PSMA, PCSA, CD63, MUC1, DLL4, CD81, B7-H3, HER 3 (ErbB3), MART-1, PSA, VEGFA, TIMP-1, GPCR GPR110, EphA2, MMP9, mmp7, TMEM211, UNC93a, BRCA, CA125 (MUC16), Mammaglobin, CD174 (Lewis y), CD66e CEA, CD24 c.sn3, C-erbB2, CD10, NGAL, epcam, CEA (carcinoembryonic Antigen), GPR30, CYFRA21-1, OPN, MUC17, hVEGFR2, MUC2, NCAM, ASPH, ErbB4, SPB, SPC, CD9, MS4A1, EphA2, MIS RII, HER2 (ErbB2), ER, PR (B), MRP8, CD63, B7H4, TGM2, CD81, DR3, STAT3, MACC-1, TrKB, IL6 Unc, OPG-13, IL6R, EZH2, SCRN1, TWEAK, SERPINB3, CDAC1, BCA-225, DR3, A33, NPGP/NPFF2, TIMP1, BDNF, FRT, Ferritin heavy chain, seprase, p53, LDH, HSP, ost, p53, CXCL12, HAP, CRP, Gro-alpha, Tsg 101, GDF15 Breast cancer CD9, HSP70, Gal3, MIS (RII), EGFR, ER, ICB3, CD63, B7H4, MUC1, CD81, ERB3, MART1, STAT3, VEGF, BCA225, BRCA, CA125, CD174, CD24, ERB2, NGAL, GPR30, CYFRA21, CD31, cMET, MUC2, ERB4, TMEM211 Breast Cancer 5T4 (trophoblast), ADAM10, AGER/RAGE, APC, APP (β-amyloid), ASPH (A- 10), B7H3 (CD276), BACE1, BAI3, BRCA1, BDNF, BIRC2, C1GALT1, CA125 (MUC16), Calmodulin 1, CCL2 (MCP-1), CD9, CD10, CD127 (IL7R), CD174, CD24, CD44, CD63, CD81, CEA, CRMP-2, CXCR3, CXCR4, CXCR6, CYFRA 21, derlin 1, DLL4, DPP6, E-CAD, EpCaM, EphA2 (H-77), ER(1) ESR1α, ER(2) ESR2β, ErbB4, Erbb2, erb3 (Erb-B3), PA2G4, FRT (FLT1), Gal3, GPR30 (G- coupled ER1), HAP1, HER3, HSP-27, HSP70, IC3b, IL8, insig, junction plakoglobin, Keratin 15, KRAS, Mammaglobin, MART1, MCT2, MFGE8, MMP9, MRP8, Muc1, MUC17, MUC2, NCAM, NG2 (CSPG4), Ngal, NHE-3, NT5E (CD73), ODC1, OPG, OPN, p53, PARK7, PCSA, PGP9.5 (PARK5), PR(B), PSA, PSMA, RAGE, STXBP4, Survivin, TFF3 (secreted), TIMP1, TIMP2, TMEM211, TRAF4 (scaffolding), TRAIL-R2 (death Receptor 5), TrkB, Tsg 101, UNC93a, VEGFA, VEGFR2, YB-1, VEGFR1, GCDPF-15 (PIP), BigH3 (TGFb1-induced protein), 5HT2B (serotonin receptor 2B), BRCA2, BACE1, CDH1-cadherin Breast Cancer AK5.2, ATP6V1B1, CRABP1 Breast Cancer DST.3, GATA3, KRT81 Breast Cancer AK5.2, ATP6V1B1, CRABP1, DST.3, ELF5, GATA3, KRT81, LALBA, OXTR, RASL10A, SERHL, TFAP2A.1, TFAP2A.3, TFAP2C, VTCN1 Breast Cancer TRAP; Renal Cell Carcinoma; Filamin; 14.3.3, Pan; Prohibitin; c-fos; Ang-2; GSTmu; Ang-1; FHIT; Rad51; Inhibin alpha; Cadherin-P; 14.3.3 gamma; p18INK4c; P504S; XRCC2; Caspase 5; CREB-Binding Protein; Estrogen Receptor; IL17; Claudin 2; Keratin 8; GAPDH; CD1; Keratin, LMW; Gamma Glutamylcysteine Synthetase(GCS)/Glutamate-cysteine Ligase; a-B-Crystallin; Pax-5; MMP-19; APC; IL-3; Keratin 8 (phospho-specific Ser73); TGF-beta 2; ITK; Oct-2/; DJ-1; B7-H2; Plasma Cell Marker; Rad18; Estriol; Chk1; Prolactin Receptor; Laminin Receptor; Histone H1; CD45RO; GnRH Receptor; IP10/CRG2; Actin, Muscle Specific; S100; Dystrophin; Tubulin-a; CD3zeta; CDC37; GABA a Receptor 1; MMP-7 (Matrilysin); Heregulin; Caspase 3; CD56/NCAM-1; Gastrin 1; SREBP-1 (Sterol Regulatory Element Binding Protein-1); MLH1; PGP9.5; Factor VIII Related Antigen; ADP-ribosylation Factor (ARF-6); MHC II (HLA-DR) Ia; Survivin; CD23; G-CSF; CD2; Calretinin; Neuron Specific Enolase; CD165; Calponin; CD95/Fas; Urocortin; Heat Shock Protein 27/hsp27; Topo II beta; Insulin Receptor; Keratin 5/8; sm; Actin, skeletal muscle; CA19-9; GluR1; GRIP1; CD79a mb-1; TdT; HRP; CD94; CCK-8; Thymidine Phosphorylase; CD57; Alkaline Phosphatase (AP); CD59/MACIF/ MIRL/Protectin; GLUT-1; alpha-1-antitrypsin; Presenillin; Mucin 3 (MUC3); pS2; 14-3-3 beta; MMP-13 (Collagenase-3); Fli-1; mGluR5; Mast Cell Chymase; Laminin B1/b1; Neurofilament (160 kDa); CNPase; Amylin Peptide; Gai1; CD6; alpha-1-antichymotrypsin; E2F-2; MyoD1 Ductal carcinoma in Laminin B1/b1; E2F-2; TdT; Apolipoprotein D; Granulocyte; Alkaline situ (DCIS) Phosphatase (AP); Heat Shock Protein 27/hsp27; CD95/Fas; pS2; Estriol; GLUT-1; Fibronectin; CD6; CCK-8; sm; Factor VIII Related Antigen; CD57; Plasminogen; CD71/Transferrin Receptor; Keratin 5/8; Thymidine Phosphorylase; CD45/T200/LCA; Epithelial Specific Antigen; Macrophage; CD10; MyoD1; Gai1; bcl-XL; hPL; Caspase 3; Actin, skeletal muscle; IP10/CRG2; GnRH Receptor; p35nck5a; ADP-ribosylation Factor (ARF-6); Cdk4; alpha-1-antitrypsin; IL17; Neuron Specific Enolase; CD56/NCAM-1; Prolactin Receptor; Cdk7; CD79a mb-1; Collagen IV; CD94; Myeloid Specific Marker; Keratin 10; Pax-5; IgM (m-Heavy Chain); CD45RO; CA19-9; Mucin 2; Glucagon; Mast Cell Chymase; MLH1; CD1; CNPase; Parkin; MHC II (HLA- DR) Ia; B7-H2; Chk1; Lambda Light Chain; MHC II (HLA-DP and DR); Myogenin; MMP-7 (Matrilysin); Topo II beta; CD53; Keratin 19; Rad18; Ret Oncoprotein; MHC II (HLA-DP); E3-binding protein (ARM1); Progesterone Receptor; Keratin 8; IgG; IgA; Tubulin; Insulin Receptor Substrate-1; Keratin 15; DR3; IL-3; Keratin 10/13; Cyclin D3; MHC I (HLA25 and HLA-Aw32); Calmodulin; Neurofilament (160 kDa) Ductal carcinoma in Macrophage; Fibronectin; Granulocyte; Keratin 19; Cyclin D3; CD45/T200/LCA; situ (DCIS) v. other EGFR; Thrombospondin; CD81/TAPA-1; Ruv C; Plasminogen; Collagen IV; Breast cancer Laminin B1/b1; CD10; TdT; Filamin; bcl-XL; 14.3.3 gamma; 14.3.3, Pan; p170; Apolipoprotein D; CD71/Transferrin Receptor; FHIT Lung cancer Pgrmc1 (progesterone receptor membrane component 1)/sigma-2 receptor, STEAP, EZH2 Lung cancer Prohibitin, CD23, Amylin Peptide, HRP, Rad51, Pax-5, Oct-3/, GLUT-1, PSCA, Thrombospondin, FHIT, a-B-Crystallin, LewisA, Vacular Endothelial Growth Factor(VEGF), Hepatocyte Factor Homologue-4, Flt-4, GluR6/7, Prostate Apoptosis Response Protein-4, GluR1, Fli-1, Urocortin, S100A4, 14-3-3 beta, P504S, HDAC1, PGP9.5, DJ-1, COX2, MMP-19, Actin, skeletal muscle, Claudin 3, Cadherin-P, Collagen IX, p27Kip1, Cathepsin D, CD30 (Reed-Sternberg Cell Marker), Ubiquitin, FSH-b, TrxR2, CCK-8, Cyclin C, CD138, TGF-beta 2, Adrenocorticotrophic Hormone, PPAR-gamma, Bcl-6, GLUT-3, IGF-I, mRANKL, Fas-ligand, Filamin, Calretinin, O ct-1, Parathyroid Hormone, Claudin 5, Claudin 4, Raf-1 (Phospho-specific), CDC14A Phosphatase, Mitochondria, APC, Gastrin 1, Ku (p80), Gai1, XPA, Maltose Binding Protein, Melanoma (gp100), Phosphotyrosine, Amyloid A, CXCR4/Fusin, Hepatic Nuclear Factor- 3B, Caspase 1, HPV 16-E7, Axonal Growth Cones, Lck, Ornithine Decarboxylase, Gamma Glutamylcysteine Synthetase(GCS)/Glutamate-cysteine Ligase, ERCC1, Calmodulin, Caspase 7 (Mch 3), CD137 (4-1BB), Nitric Oxide Synthase, brain (bNOS), E2F-2, IL-10R, L-Plastin, CD18, Vimentin, CD50/ICAM-3, Superoxide Dismutase, Adenovirus Type 5 E1A, PHAS-I, Progesterone Receptor (phospho-specific) - Serine 294, MHC II (HLA-DQ), XPG, ER Ca+2 ATPase2, Laminin-s, E3-binding protein (ARM1), CD45RO, CD1, Cdk2, MMP-10 (Stromilysin-2), sm, Surfactant Protein B (Pro), Apolipoprotein D, CD46, Keratin 8 (phospho-specific Ser73), PCNA, PLAP, CD20, Syk, LH, Keratin 19, ADP-ribosylation Factor (ARF-6), Int-2 Oncoprotein, Luciferase, AIF (Apoptosis Inducing Factor), Grb2, bcl-X, CD16, Paxillin, MHC II (HLA-DP and DR), B-Cell, p21WAF1, MHC II (HLA-DR), Tyrosinase, E2F-1, Pds1, Calponin, Notch, CD26/DPP IV, SV40 Large T Antigen, Ku (p70/p80), Perforin, XPF, SIM Ag (SIMA-4D3), Cdk1/p34cdc2, Neuron Specific Enolase, b-2-Microglobulin, DNA Polymerase Beta, Thyroid Hormone Receptor, Human, Alkaline Phosphatase (AP), Plasma Cell Marker, Heat Shock Protein 70/hsp70, TRP75/ gp75, SRF (Serum Response Factor), Laminin B1/b1, Mast Cell Chymase, Caldesmon, CEA/CD66e, CD24, Retinoid X Receptor (hRXR), CD45/T200/LCA, Rabies Virus, Cytochrome c, DR3, bcl-XL, Fascin, CD71/ Transferrin Receptor Integrins ITGA1 (CD49a, VLA1), ITGA2 (CD49b, VLA2), ITGA3 (CD49c, VLA3), ITGA4 (CD49d, VLA4), ITGA5 (CD49e, VLA5), ITGA6 (CD49f, VLA6), ITGA7 (FLJ25220), ITGA8, ITGA9 (RLC), ITGA10, ITGA11 (HsT18964), ITGAD (CD11D, FLJ39841), ITGAE (CD103, HUMINAE), ITGAL (CD11a, LFA1A), ITGAM (CD11b, MAC-1), ITGAV (CD51, VNRA, MSK8), ITGAW, ITGAX (CD11c), ITGB1 (CD29, FNRB, MSK12, MDF20), ITGB2 (CD18, LFA- 1, MAC-1, MFI7), ITGB3 (CD61, GP3A, GPIIIa), ITGB4 (CD104), ITGB5 (FLJ26658), ITGB6, ITGB7, ITGB8 Glycoprotein GpIa-IIa, GpIIb-IIIa, GpIIIb, GpIb, GpIX Transcription factors STAT3, EZH2, p53, MACC1, SPDEF, RUNX2, YB-1 Kinases AURKA, AURKB Disease Markers 6Ckine, Adiponectin, Adrenocorticotropic Hormone, Agouti-Related Protein, Aldose Reductase, Alpha-1-Antichymotrypsin, Alpha-1-Antitrypsin, Alpha-1- Microglobulin, Alpha-2-Macroglobulin, Alpha-Fetoprotein, Amphiregulin, Angiogenin, Angiopoietin-2, Angiotensin-Converting Enzyme, Angiotensinogen, Annexin A1, Apolipoprotein A-I, Apolipoprotein A-II, Apolipoprotein A-IV, Apolipoprotein B, Apolipoprotein C-I, Apolipoprotein C-III, Apolipoprotein D, Apolipoprotein E, Apolipoprotein H, Apolipoprotein(a), AXL Receptor Tyrosine Kinase, B cell-activating Factor, B Lymphocyte Chemoattractant, Bcl-2-like protein 2, Beta-2-Microglobulin, Betacellulin, Bone Morphogenetic Protein 6, Brain-Derived Neurotrophic Factor, Calbindin, Calcitonin, Cancer Antigen 125, Cancer Antigen 15-3, Cancer Antigen 19-9, Cancer Antigen 72-4, Carcinoembryonic Antigen, Cathepsin D, CD 40 antigen, CD40 Ligand, CD5 Antigen-like, Cellular Fibronectin, Chemokine CC-4, Chromogranin-A, Ciliary Neurotrophic Factor, Clusterin, Collagen IV, Complement C3, Complement Factor H, Connective Tissue Growth Factor, Cortisol, C-Peptide, C-Reactive Protein, Creatine Kinase-MB, Cystatin-C, Endoglin, Endostatin, Endothelin-1, EN-RAGE, Eotaxin-1, Eotaxin-2, Eotaxin-3, Epidermal Growth Factor, Epiregulin, Epithelial cell adhesion molecule, Epithelial-Derived Neutrophil- Activating Protein 78, Erythropoietin, E-Selectin, Ezrin, Factor VII, Fas Ligand, FASLG Receptor, Fatty Acid-Binding Protein (adipocyte), Fatty Acid-Binding Protein (heart), Fatty Acid-Binding Protein (liver), Ferritin, Fetuin-A, Fibrinogen, Fibroblast Growth Factor 4, Fibroblast Growth Factor basic, Fibulin-1C, Follicle- Stimulating Hormone, Galectin-3, Gelsolin, Glucagon, Glucagon-like Peptide 1, Glucose-6-phosphate Isomerase, Glutamate-Cysteine Ligase Regulatory subunit, Glutathione S-Transferase alpha, Glutathione S-Transferase Mu 1, Granulocyte Colony-Stimulating Factor, Granulocyte-Macrophage Colony-Stimulating Factor, Growth Hormone, Growth-Regulated alpha protein, Haptoglobin, HE4, Heat Shock Protein 60, Heparin-Binding EGF-Like Growth Factor, Hepatocyte Growth Factor, Hepatocyte Growth Factor Receptor, Hepsin, Human Chorionic Gonadotropin beta, Human Epidermal Growth Factor Receptor 2, Immunoglobulin A, Immunoglobulin E, Immunoglobulin M, Insulin, Insulin-like Growth Factor I, Insulin-like Growth Factor-Binding Protein 1, Insulin-like Growth Factor-Binding Protein 2, Insulin-like Growth Factor-Binding Protein 3, Insulin-like Growth Factor Binding Protein 4, Insulin-like Growth Factor Binding Protein 5, Insulin-like Growth Factor Binding Protein 6, Intercellular Adhesion Molecule 1, Interferon gamma, Interferon gamma Induced Protein 10, Interferon- inducible T-cell alpha chemoattractant, Interleukin-1 alpha, Interleukin-1 beta, Interleukin-1 Receptor antagonist, Interleukin-2, Interleukin-2 Receptor alpha, Interleukin-3, Interleukin-4, Interleukin-5, Interleukin-6, Interleukin-6 Receptor, Interleukin-6 Receptor subunit beta, Interleukin-7, Interleukin-8, Interleukin-10, Interleukin-11, Interleukin-12 Subunit p40, Interleukin-12 Subunit p70, Interleukin-13, Interleukin-15, Interleukin-16, Interleukin-25, Kallikrein 5, Kallikrein-7, Kidney Injury Molecule-1, Lactoylglutathione lyase, Latency- Associated Peptide of Transforming Growth Factor beta 1, Lectin-Like Oxidized LDL Receptor 1, Leptin, Luteinizing Hormone, Lymphotactin, Macrophage Colony-Stimulating Factor 1, Macrophage Inflammatory Protein-1 alpha, Macrophage Inflammatory Protein-1 beta, Macrophage Inflammatory Protein-3 alpha, Macrophage inflammatory protein 3 beta, Macrophage Migration Inhibitory Factor, Macrophage-Derived Chemokine, Macrophage-Stimulating Protein, Malondialdehyde-Modified Low-Density Lipoprotein, Maspin, Matrix Metalloproteinase-1, Matrix Metalloproteinase-2, Matrix Metalloproteinase-3, Matrix Metalloproteinase-7, Matrix Metalloproteinase-9, Matrix Metalloproteinase-9, Matrix Metalloproteinase-10, Mesothelin, MHC class I chain-related protein A, Monocyte Chemotactic Protein 1, Monocyte Chemotactic Protein 2, Monocyte Chemotactic Protein 3, Monocyte Chemotactic Protein 4, Monokine Induced by Gamma Interferon, Myeloid Progenitor Inhibitory Factor 1, Myeloperoxidase, Myoglobin, Nerve Growth Factor beta, Neuronal Cell Adhesion Molecule, Neuron-Specific Enolase, Neuropilin-1, Neutrophil Gelatinase- Associated Lipocalin, NT-proBNP, Nucleoside diphosphate kinase B, Osteopontin, Osteoprotegerin, Pancreatic Polypeptide, Pepsinogen I, Peptide YY, Peroxiredoxin-4, Phosphoserine Aminotransferase, Placenta Growth Factor, Plasminogen Activator Inhibitor 1, Platelet-Derived Growth Factor BB, Pregnancy-Associated Plasma Protein A, Progesterone, Proinsulin (inc. Total or Intact), Prolactin, Prostasin, Prostate-Specific Antigen (inc. Free PSA), Prostatic Acid Phosphatase, Protein S100-A4, Protein S100-A6, Pulmonary and Activation- Regulated Chemokine, Receptor for advanced glycosylation end products, Receptor tyrosine-protein kinase erbB-3, Resistin, S100 calcium-binding protein B, Secretin, Serotransferrin, Serum Amyloid P-Component, Serum Glutamic Oxaloacetic Transaminase, Sex Hormone-Binding Globulin, Sortilin, Squamous Cell Carcinoma Antigen-1, Stem Cell Factor, Stromal cell-derived Factor-1, Superoxide Dismutase 1 (soluble), T Lymphocyte-Secreted Protein I-309, Tamm- Horsfall Urinary Glycoprotein, T-Cell-Specific Protein RANTES, Tenascin-C, Testosterone, Tetranectin, Thrombomodulin, Thrombopoietin, Thrombospondin-1, Thyroglobulin, Thyroid-Stimulating Hormone, Thyroxine-Binding Globulin, Tissue Factor, Tissue Inhibitor of Metalloproteinases 1, Tissue type Plasminogen activator, TNF-Related Apoptosis-Inducing Ligand Receptor 3, Transforming Growth Factor alpha, Transforming Growth Factor beta-3, Transthyretin, Trefoil Factor 3, Tumor Necrosis Factor alpha, Tumor Necrosis Factor beta, Tumor Necrosis Factor Receptor I, Tumor necrosis Factor Receptor 2, Tyrosine kinase with Ig and EGF homology domains 2, Urokinase-type Plasminogen Activator, Urokinase-type plasminogen activator Receptor, Vascular Cell Adhesion Molecule-1, Vascular Endothelial Growth Factor, Vascular endothelial growth Factor B, Vascular Endothelial Growth Factor C, Vascular endothelial growth Factor D, Vascular Endothelial Growth Factor Receptor 1, Vascular Endothelial Growth Factor Receptor 2, Vascular endothelial growth Factor Receptor 3, Vitamin K-Dependent Protein S, Vitronectin, von Willebrand Factor, YKL-40 Disease Markers Adiponectin, Adrenocorticotropic Hormone, Agouti-Related Protein, Alpha-1- Antichymotrypsin, Alpha-1-Antitrypsin, Alpha-1-Microglobulin, Alpha-2- Macroglobulin, Alpha-Fetoprotein, Amphiregulin, Angiopoietin-2, Angiotensin- Converting Enzyme, Angiotensinogen, Apolipoprotein A-I, Apolipoprotein A-II, Apolipoprotein A-IV, Apolipoprotein B, Apolipoprotein C-I, Apolipoprotein C- III, Apolipoprotein D, Apolipoprotein E, Apolipoprotein H, Apolipoprotein(a), AXL Receptor Tyrosine Kinase, B Lymphocyte Chemoattractant, Beta-2- Microglobulin, Betacellulin, Bone Morphogenetic Protein 6, Brain-Derived Neurotrophic Factor, Calbindin, Calcitonin, Cancer Antigen 125, Cancer Antigen 19-9, Carcinoembryonic Antigen, CD 40 antigen, CD40 Ligand, CD5 Antigen- like, Chemokine CC-4, Chromogranin-A, Ciliary Neurotrophic Factor, Clusterin, Complement C3, Complement Factor H, Connective Tissue Growth Factor, Cortisol, C-Peptide, C-Reactive Protein, Creatine Kinase-MB, Cystatin-C, Endothelin-1, EN-RAGE, Eotaxin-1, Eotaxin-3, Epidermal Growth Factor, Epiregulin, Epithelial-Derived Neutrophil-Activating Protein 78, Erythropoietin, E-Selectin, Factor VII, Fas Ligand, FASLG Receptor, Fatty Acid-Binding Protein (heart), Ferritin, Fetuin-A, Fibrinogen, Fibroblast Growth Factor 4, Fibroblast Growth Factor basic, Follicle-Stimulating Hormone, Glucagon, Glucagon-like Peptide 1, Glutathione S-Transferase alpha, Granulocyte Colony-Stimulating Factor, Granulocyte-Macrophage Colony-Stimulating Factor, Growth Hormone, Growth-Regulated alpha protein, Haptoglobin, Heat Shock Protein 60, Heparin- Binding EGF-Like Growth Factor, Hepatocyte Growth Factor, Immunoglobulin A, Immunoglobulin E, Immunoglobulin M, Insulin, Insulin-like Growth Factor I, Insulin-like Growth Factor-Binding Protein 2, Intercellular Adhesion Molecule 1, Interferon gamma, Interferon gamma Induced Protein 10, Interleukin-1 alpha, Interleukin-1 beta, Interleukin-1 Receptor antagonist, Interleukin-2, Interleukin-3, Interleukin-4, Interleukin-5, Interleukin-6, Interleukin-6 Receptor, Interleukin-7, Interleukin-8, Interleukin-10, Interleukin-11, Interleukin-12 Subunit p40, Interleukin-12 Subunit p70, Interleukin-13, Interleukin-15, Interleukin-16, Interleukin-25, Kidney Injury Molecule-1, Lectin-Like Oxidized LDL Receptor 1, Leptin, Luteinizing Hormone, Lymphotactin, Macrophage Colony-Stimulating Factor 1, Macrophage Inflammatory Protein-1 alpha, Macrophage Inflammatory Protein-1 beta, Macrophage Inflammatory Protein-3 alpha, Macrophage Migration Inhibitory Factor, Macrophage-Derived Chemokine, Malondialdehyde-Modified Low-Density Lipoprotein, Matrix Metalloproteinase-1, Matrix Metalloproteinase- 2, Matrix Metalloproteinase-3, Matrix Metalloproteinase-7, Matrix Metalloproteinase-9, Matrix Metalloproteinase-9, Matrix Metalloproteinase-10, Monocyte Chemotactic Protein 1, Monocyte Chemotactic Protein 2, Monocyte Chemotactic Protein 3, Monocyte Chemotactic Protein 4, Monokine Induced by Gamma Interferon, Myeloid Progenitor Inhibitory Factor 1, Myeloperoxidase, Myoglobin, Nerve Growth Factor beta, Neuronal Cell Adhesion Molecule, Neutrophil Gelatinase-Associated Lipocalin, NT-proBNP, Osteopontin, Pancreatic Polypeptide, Peptide YY, Placenta Growth Factor, Plasminogen Activator Inhibitor 1, Platelet-Derived Growth Factor BB, Pregnancy-Associated Plasma Protein A, Progesterone, Proinsulin (inc. Intact or Total), Prolactin, Prostate- Specific Antigen (inc. Free PSA), Prostatic Acid Phosphatase, Pulmonary and Activation-Regulated Chemokine, Receptor for advanced glycosylation end products, Resistin, S100 calcium-binding protein B, Secretin, Serotransferrin, Serum Amyloid P-Component, Serum Glutamic Oxaloacetic Transaminase, Sex Hormone-Binding Globulin, Sortilin, Stem Cell Factor, Superoxide Dismutase 1 (soluble), T Lymphocyte-Secreted Protein 1-309, Tamm-Horsfall Urinary Glycoprotein, T-Cell-Specific Protein RANTES, Tenascin-C, Testosterone, Thrombomodulin, Thrombopoietin, Thrombospondin-1, Thyroid-Stimulating Hormone, Thyroxine-Binding Globulin, Tissue Factor, Tissue Inhibitor of Metalloproteinases 1, TNF-Related Apoptosis-Inducing Ligand Receptor 3, Transforming Growth Factor alpha, Transforming Growth Factor beta-3, Transthyretin, Trefoil Factor 3, Tumor Necrosis Factor alpha, Tumor Necrosis Factor beta, Tumor necrosis Factor Receptor 2, Vascular Cell Adhesion Molecule- 1, Vascular Endothelial Growth Factor, Vitamin K-Dependent Protein S, Vitronectin, von Willebrand Factor Oncology 6Ckine, Aldose Reductase, Alpha-Fetoprotein, Amphiregulin, Angiogenin, Annexin A1, B cell-activating Factor, B Lymphocyte Chemoattractant, Bcl-2-like protein 2, Betacellulin, Cancer Antigen 125, Cancer Antigen 15-3, Cancer Antigen 19-9, Cancer Antigen 72-4, Carcinoembryonic Antigen, Cathepsin D, Cellular Fibronectin, Collagen IV, Endoglin, Endostatin, Eotaxin-2, Epidermal Growth Factor, Epiregulin, Epithelial cell adhesion molecule, Ezrin, Fatty Acid-Binding Protein (adipocyte), Fatty Acid-Binding Protein (liver), Fibroblast Growth Factor basic, Fibulin-1C, Galectin-3, Gelsolin, Glucose-6-phosphate Isomerase, Glutamate-Cysteine Ligase Regulatory subunit, Glutathione S-Transferase Mu 1, HE4, Heparin-Binding EGF-Like Growth Factor, Hepatocyte Growth Factor, Hepatocyte Growth Factor Receptor, Hepsin, Human Chorionic Gonadotropin beta, Human Epidermal Growth Factor Receptor 2, Insulin-like Growth Factor- Binding Protein 1, Insulin-like Growth Factor-Binding Protein 2, Insulin-like Growth Factor-Binding Protein 3, Insulin-like Growth Factor Binding Protein 4, Insulin-like Growth Factor Binding Protein 5, Insulin-like Growth Factor Binding Protein 6, Interferon gamma Induced Protein 10, Interferon-inducible T-cell alpha chemoattractant, Interleukin-2 Receptor alpha, Interleukin-6, Interleukin-6 Receptor subunit beta, Kallikrein 5, Kallikrein-7, Lactoylglutathione lyase, Latency-Associated Peptide of Transforming Growth Factor beta 1, Leptin, Macrophage inflammatory protein 3 beta, Macrophage Migration Inhibitory Factor, Macrophage-Stimulating Protein, Maspin, Matrix Metalloproteinase-2, Mesothelin, MHC class I chain-related protein A, Monocyte Chemotactic Protein 1, Monokine Induced by Gamma Interferon, Neuron-Specific Enolase, Neuropilin- 1, Neutrophil Gelatinase-Associated Lipocalin, Nucleoside diphosphate kinase B, Osteopontin, Osteoprotegerin, Pepsinogen I, Peroxiredoxin-4, Phosphoserine Aminotransferase, Placenta Growth Factor, Platelet-Derived Growth Factor BB, Prostasin, Protein S100-A4, Protein S100-A6, Receptor tyrosine-protein kinase erbB-3, Squamous Cell Carcinoma Antigen-1, Stromal cell-derived Factor-1, Tenascin-C, Tetranectin, Thyroglobulin, Tissue type Plasminogen activator, Transforming Growth Factor alpha, Tumor Necrosis Factor Receptor I, Tyrosine kinase with Ig and EGF homology domains 2, Urokinase-type Plasminogen Activator, Urokinase-type plasminogen activator Receptor, Vascular Endothelial Growth Factor, Vascular endothelial growth Factor B, Vascular Endothelial Growth Factor C, Vascular endothelial growth Factor D, Vascular Endothelial Growth Factor Receptor 1, Vascular Endothelial Growth Factor Receptor 2, Vascular endothelial growth Factor Receptor 3, YKL-40 Disease Adiponectin, Alpha-1-Antitrypsin, Alpha-2-Macroglobulin, Alpha-Fetoprotein, Apolipoprotein A-I, Apolipoprotein C-III, Apolipoprotein H, Apolipoprotein(a), Beta-2-Microglobulin, Brain-Derived Neurotrophic Factor, Calcitonin, Cancer Antigen 125, Cancer Antigen 19-9, Carcinoembryonic Antigen, CD 40 antigen, CD40 Ligand, Complement C3, C-Reactive Protein, Creatine Kinase-MB, Endothelin-1, EN-RAGE, Eotaxin-1, Epidermal Growth Factor, Epithelial- Derived Neutrophil-Activating Protein 78, Erythropoietin, Factor VII, Fatty Acid- Binding Protein (heart), Ferritin, Fibrinogen, Fibroblast Growth Factor basic, Granulocyte Colony-Stimulating Factor, Granulocyte-Macrophage Colony- Stimulating Factor, Growth Hormone, Haptoglobin, Immunoglobulin A, Immunoglobulin E, Immunoglobulin M, Insulin, Insulin-like Growth Factor I, Intercellular Adhesion Molecule 1, Interferon gamma, Interleukin-1 alpha, Interleukin-1 beta, Interleukin-1 Receptor antagonist, Interleukin-2, Interleukin-3, Interleukin-4, Interleukin-5, Interleukin-6, Interleukin-7, Interleukin-8, Interleukin-10, Interleukin-12 Subunit p40, Interleukin-12 Subunit p70, Interleukin-13, Interleukin-15, Interleukin-16, Leptin, Lymphotactin, Macrophage Inflammatory Protein-1 alpha, Macrophage Inflammatory Protein-1 beta, Macrophage-Derived Chemokine, Matrix Metalloproteinase-2, Matrix Metalloproteinase-3, Matrix Metalloproteinase-9, Monocyte Chemotactic Protein 1, Myeloperoxidase, Myoglobin, Plasminogen Activator Inhibitor 1, Pregnancy- Associated Plasma Protein A, Prostate-Specific Antigen (inc. Free PSA), Prostatic Acid Phosphatase, Serum Amyloid P-Component, Serum Glutamic Oxaloacetic Transaminase, Sex Hormone-Binding Globulin, Stem Cell Factor, T-Cell-Specific Protein RANTES, Thrombopoietin, Thyroid-Stimulating Hormone, Thyroxine- Binding Globulin, Tissue Factor, Tissue Inhibitor of Metalloproteinases 1, Tumor Necrosis Factor alpha, Tumor Necrosis Factor beta, Tumor Necrosis Factor Receptor 2, Vascular Cell Adhesion Molecule-1, Vascular Endothelial Growth Factor, von Willebrand Factor Neurological Alpha-1-Antitrypsin, Apolipoprotein A-I, Apolipoprotein A-II, Apolipoprotein B, Apolipoprotein C-I, Apolipoprotein H, Beta-2-Microglobulin, Betacellulin, Brain- Derived Neurotrophic Factor, Calbindin, Cancer Antigen 125, Carcinoembryonic Antigen, CD5 Antigen-like, Complement C3, Connective Tissue Growth Factor, Cortisol, Endothelin-1, Epidermal Growth Factor Receptor, Ferritin, Fetuin-A, Follicle-Stimulating Hormone, Haptoglobin, Immunoglobulin A, Immunoglobulin M, Intercellular Adhesion Molecule 1, Interleukin-6 Receptor, Interleukin-7, Interleukin-10, Interleukin-11, Interleukin-17, Kidney Injury Molecule-1, Luteinizing Hormone, Macrophage-Derived Chemokine, Macrophage Migration Inhibitory Factor, Macrophage Inflammatory Protein-1 alpha, Matrix Metalloproteinase-2, Monocyte Chemotactic Protein 2, Peptide YY, Prolactin, Prostatic Acid Phosphatase, Serotransferrin, Serum Amyloid P-Component, Sortilin, Testosterone, Thrombopoietin, Thyroid-Stimulating Hormone, Tissue Inhibitor of Metalloproteinases 1, TNF-Related Apoptosis-Inducing Ligand Receptor 3, Tumor necrosis Factor Receptor 2, Vascular Endothelial Growth Factor, Vitronectin Cardiovascular Adiponectin, Apolipoprotein A-I, Apolipoprotein B, Apolipoprotein C-III, Apolipoprotein D, Apolipoprotein E, Apolipoprotein H, Apolipoprotein(a), Clusterin, C-Reactive Protein, Cystatin-C, EN-RAGE, E-Selectin, Fatty Acid- Binding Protein (heart), Ferritin, Fibrinogen, Haptoglobin, Immunoglobulin M, Intercellular Adhesion Molecule 1, Interleukin-6, Interleukin-8, Lectin-Like Oxidized LDL Receptor 1, Leptin, Macrophage Inflammatory Protein-1 alpha, Macrophage Inflammatory Protein-1 beta, Malondialdehyde-Modified Low- Density Lipoprotein, Matrix Metalloproteinase-1, Matrix Metalloproteinase-10, Matrix Metalloproteinase-2, Matrix Metalloproteinase-3, Matrix Metalloproteinase-7, Matrix Metalloproteinase-9, Monocyte Chemotactic Protein 1, Myeloperoxidase, Myoglobin, NT-proBNP, Osteopontin, Plasminogen Activator Inhibitor 1, P-Selectin, Receptor for advanced glycosylation end products, Serum Amyloid P-Component, Sex Hormone-Binding Globulin, T-Cell- Specific Protein RANTES, Thrombomodulin, Thyroxine-Binding Globulin, Tissue Inhibitor of Metalloproteinases 1, Tumor Necrosis Factor alpha, Tumor necrosis Factor Receptor 2, Vascular Cell Adhesion Molecule-1, von Willebrand Factor Inflammatory Alpha-1-Antitrypsin, Alpha-2-Macroglobulin, Beta-2-Microglobulin, Brain- Derived Neurotrophic Factor, Complement C3, C-Reactive Protein, Eotaxin-1, Factor VII, Ferritin, Fibrinogen, Granulocyte-Macrophage Colony-Stimulating Factor, Haptoglobin, Intercellular Adhesion Molecule 1, Interferon gamma, Interleukin-1 alpha, Interleukin-1 beta, Interleukin-1 Receptor antagonist, Interleukin-2, Interleukin-3, Interleukin-4, Interleukin-5, Interleukin-6, Interleukin-7, Interleukin-8, Interleukin-10, Interleukin-12 Subunit p40, Interleukin-12 Subunit p70, Interleukin-15, Interleukin-17, Interleukin-23, Macrophage Inflammatory Protein-1 alpha, Macrophage Inflammatory Protein-1 beta, Matrix Metalloproteinase-2, Matrix Metalloproteinase-3, Matrix Metalloproteinase-9, Monocyte Chemotactic Protein 1, Stem Cell Factor, T-Cell- Specific Protein RANTES, Tissue Inhibitor of Metalloproteinases 1, Tumor Necrosis Factor alpha, Tumor Necrosis Factor beta, Tumor necrosis Factor Receptor 2, Vascular Cell Adhesion Molecule-1, Vascular Endothelial Growth Factor, Vitamin D-Binding Protein, von Willebrand Factor Metabolic Adiponectin, Adrenocorticotropic Hormone, Angiotensin-Converting Enzyme, Angiotensinogen, Complement C3 alpha des arg, Cortisol, Follicle-Stimulating Hormone, Galanin, Glucagon, Glucagon-like Peptide 1, Insulin, Insulin-like Growth Factor I, Leptin, Luteinizing Hormone, Pancreatic Polypeptide, Peptide YY, Progesterone, Prolactin, Resistin, Secretin, Testosterone Kidney Alpha-1-Microglobulin, Beta-2-Microglobulin, Calbindin, Clusterin, Connective Tissue Growth Factor, Creatinine, Cystatin-C, Glutathione S-Transferase alpha, Kidney Injury Molecule-1, Microalbumin, Neutrophil Gelatinase-Associated Lipocalin, Osteopontin, Tamm-Horsfall Urinary Glycoprotein, Tissue Inhibitor of Metalloproteinases 1, Trefoil Factor 3, Vascular Endothelial Growth Factor Cytokines Granulocyte-Macrophage Colony-Stimulating Factor, Interferon gamma, Interleukin-2, Interleukin-3, Interleukin-4, Interleukin-5, Interleukin-6, Interleukin-7, Interleukin-8, Interleukin-10, Macrophage Inflammatory Protein-1 alpha, Macrophage Inflammatory Protein-1 beta, Matrix Metalloproteinase-2, Monocyte Chemotactic Protein 1, Tumor Necrosis Factor alpha, Tumor Necrosis Factor beta, Brain-Derived Neurotrophic Factor, Eotaxin-1, Intercellular Adhesion Molecule 1, Interleukin-1 alpha, Interleukin-1 beta, Interleukin-1 Receptor antagonist, Interleukin-12 Subunit p40, Interleukin-12 Subunit p70, Interleukin- 15, Interleukin-17, Interleukin-23, Matrix Metalloproteinase-3, Stem Cell Factor, Vascular Endothelial Growth Factor Protein 14.3.3 gamma, 14.3.3 (Pan), 14-3-3 beta, 6-Histidine, a-B-Crystallin, Acinus, Actin beta, Actin (Muscle Specific), Actin (Pan), Actin (skeletal muscle), Activin Receptor Type II, Adenovirus, Adenovirus Fiber, Adenovirus Type 2 E1A, Adenovirus Type 5 E1A, ADP-ribosylation Factor (ARF-6), Adrenocorticotrophic Hormone, AIF (Apoptosis Inducing Factor), Alkaline Phosphatase (AP), Alpha Fetoprotein (AFP), Alpha Lactalbumin, alpha-1-antichymotrypsin, alpha-1- antitrypsin, Amphiregulin, Amylin Peptide, Amyloid A, Amyloid A4 Protein Precursor, Amyloid Beta (APP), Androgen Receptor, Ang-1, Ang-2, APC, APC11, APC2, Apolipoprotein D, A-Raf, ARC, Ask1/MAPKKK5, ATM, Axonal Growth Cones, b Galactosidase, b-2-Microglobulin, B7-H2, BAG-1, Bak, Bax, B-Cell, B-cell Linker Protein (BLNK), Bc110/CIPER/CLAP/mE10, bcl- 2a, Bcl-6, bcl-X, bcl-XL, Bim (BOD), Biotin, Bonzo/STRL33/TYMSTR, Bovine Serum Albumin, BRCA2 (aa 1323-1346), BrdU, Bromodeoxyuridine (BrdU), CA125, CA19-9, c-Abl, Cadherin (Pan), Cadherin-E, Cadherin-P, Calcitonin, Calcium Pump ATPase, Caldesmon, Calmodulin, Calponin, Calretinin, Casein, Caspase 1, Caspase 2, Caspase 3, Caspase 5, Caspase 6 (Mch 2), Caspase 7 (Mch 3), Caspase 8 (FLICE), Caspase 9, Catenin alpha, Catenin beta, Catenin gamma, Cathepsin D, CCK-8, CD1, CD10, CD100/Leukocyte Semaphorin, CD105, CD106/VCAM, CD115/c-fms/CSF-1R/M-CSFR, CD137 (4-1BB), CD138, CD14, CD15, CD155/PVR (Polio Virus Receptor), CD16, CD165, CD18, CD1a, CD1b, CD2, CD20, CD21, CD23, CD231, CD24, CD25/IL-2 Receptor a, CD26/DPP IV, CD29, CD30 (Reed-Sternberg Cell Marker), CD32/Fcg Receptor II, CD35/CR1, CD36GPIIIb/GPIV, CD3zeta, CD4, CD40, CD42b, CD43, CD45/T200/LCA, CD45RB, CD45RO, CD46, CD5, CD50/ICAM-3, CD53, CD54/ICAM-1, CD56/NCAM-1, CD57, CD59/MACIF/MIRL/Protectin, CD6, CD61/Platelet Glycoprotein IIIA, CD63, CD68, CD71/Transferrin Receptor, CD79a mb-1, CD79b, CD8, CD81/TAPA-1, CD84, CD9, CD94, CD95/Fas, CD98, CDC14A Phosphatase, CDC25C, CDC34, CDC37, CDC47, CDC6, cdh1, Cdk1/p34cdc2, Cdk2, Cdk3, Cdk4, Cdk5, Cdk7, Cdk8, CDw17, CDw60, CDw75, CDw78, CEA/CD66e, c-erbB-2/HER-2/neu Ab-1 (21N), c-erbB-4/HER-4, c-fos, Chk1, Chorionic Gonadotropin beta (hCG-beta), Chromogranin A, CIDE-A, CIDE-B, CITED1, c-jun, Clathrin, claudin 11, Claudin 2, Claudin 3, Claudin 4, Claudin 5, CLAUDIN 7, Claudin-1, CNPase, Collagen II, Collagen IV, Collagen IX, Collagen VII, Connexin 43, COX2, CREB, CREB-Binding Protein, Cryptococcus neoformans, c-Src, Cullin-1 (CUL-1), Cullin-2 (CUL-2), Cullin-3 (CUL-3), CXCR4/Fusin, Cyclin B1, Cyclin C, Cyclin D1, Cyclin D3, Cyclin E, Cyclin E2, Cystic Fibrosis Transmembrane Regulator, Cytochrome c, D4-GDI, Daxx, DcR1, DcR2/TRAIL-R4/TRUNDD, Desmin, DFF40 (DNA Fragmentation Factor 40)/CAD, DFF45/ICAD, DJ-1, DNA Ligase I, DNA Polymerase Beta, DNA Polymerase Gamma, DNA Primase (p49), DNA Primase (p58), DNA-PKcs, DP-2, DR3, DRS, Dysferlin, Dystrophin, E2F-1, E2F-2, E2F-3, E2F-4, E2F-5, E3-binding protein (ARM1), EGFR, EMA/CA15-3/MUC-1, Endostatin, Epithelial Membrane Antigen (EMA/CA15-3/MUC-1), Epithelial Specific Antigen, ER beta, ER Ca+2 ATPase2, ERCC1, Erk1, ERK2, Estradiol, Estriol, Estrogen Receptor, Exo1, Ezrin/p81/80K/Cytovillin, F.VIII/VWF, Factor VIII Related Antigen, FADD (FAS-Associated death domain-containing protein), Fascin, Fas-ligand, Ferritin, FGF-1, FGF-2, FHIT, Fibrillin-1, Fibronectin, Filaggrin, Filamin, FITC, Fli-1, FLIP, Flk-1/KDR/VEGFR2, Flt-1/VEGFR1, Flt-4, Fra2, FSH, FSH-b, Fyn, Ga0, Gab-1, GABA a Receptor 1, GAD65, Gai1, Gamma Glutamyl Transferase (gGT), Gamma Glutamylcysteine Synthetase(GCS)/Glutamate-cysteine Ligase, GAPDH, Gastrin 1, GCDFP-15, G- CSF, GFAP, Glicentin, Glucagon, Glucose-Regulated Protein 94, GluR 2/3, GluR1, GluR4, GluR6/7, GLUT-1, GLUT-3, Glycogen Synthase Kinase 3b (GSK3b), Glycophorin A, GM-CSF, GnRH Receptor, Golgi Complex, Granulocyte, Granzyme B, Grb2, Green Fluorescent Protein (GFP), GRIP1, Growth Hormone (hGH), GSK-3, GST, GSTmu, H.Pylori, HDAC1, HDJ- 2/DNAJ, Heat Shock Factor 1, Heat Shock Factor 2, Heat Shock Protein 27/hsp27, Heat Shock Protein 60/hsp60, Heat Shock Protein 70/hsp70, Heat Shock Protein 75/hsp75, Heat Shock Protein 90a/hsp86, Heat Shock Protein 90b/hsp84, Helicobacter pylori, Heparan Sulfate Proteoglycan, Hepatic Nuclear Factor-3B, Hepatocyte, Hepatocyte Factor Homologue-4, Hepatocyte Growth Factor, Heregulin, HIF-1a, Histone H1, hPL, HPV 16, HPV 16-E7, HRP, Human Sodium Iodide Symporter (hNIS), I-FLICE/CASPER, IFN gamma, IgA, IGF-1R, IGF-I, IgG, IgM (m-Heavy Chain), I-Kappa-B Kinase b (IKKb), IL-1 alpha, IL-1 beta, IL-10, IL-10R, IL17, IL-2, IL-3, IL-30, IL-4, IL-5, IL-6, IL-8, Inhibin alpha, Insulin, Insulin Receptor, Insulin Receptor Substrate-1, Int-2 Oncoprotein, Integrin beta5, Interferon-a(II), Interferon-g, Involucrin, IP10/CRG2, IPO-38 Proliferation Marker, IRAK, ITK, JNK Activating kinase (JKK1), Kappa Light Chain, Keratin 10, Keratin 10/13, Keratin 14, Keratin 15, Keratin 16, Keratin 18, Keratin 19, Keratin 20, Keratin 5/6/18, Keratin 5/8, Keratin 8, Keratin 8 (phospho- specific Ser73), Keratin 8/18, Keratin (LMW), Keratin (Multi), Keratin (Pan), Ki67, Ku (p70/p80), Ku (p80), L1 Cell Adhesion Molecule, Lambda Light Chain, Laminin B1/b1, Laminin B2/g1, Laminin Receptor, Laminin-s, Lck, Lck (p561ck), Leukotriene (C4, D4, E4), LewisA, LewisB, LH, L-Plastin, LRP/MVP, Luciferase, Macrophage, MADD, MAGE-1, Maltose Binding Protein, MAP1B, MAP2a, b, MART-1/Melan-A, Mast Cell Chymase, Mcl-1, MCM2, MCM5, MDM2, Medroxyprogesterone Acetate (MPA), Mek1, Mek2, Mek6, Mekk-1, Melanoma (gp100), mGluR1, mGluR5, MGMT, MHC I (HLA25 and HLA- Aw32), MHC I (HLA-A), MHC I (HLA-A, B, C), MHC I (HLA-B), MHC II (HLA-DP and DR), MHC II (HLA-DP), MHC II (HLA-DQ), MHC II (HLA-DR), MHC II (HLA-DR) Ia, Microphthalmia, Milk Fat Globule Membrane Protein, Mitochondria, MLH1, MMP-1 (Collagenase-I), MMP-10 (Stromilysin-2), MMP- 11 (Stromelysin-3), MMP-13 (Collagenase-3), MMP-14/MT1-MMP, MMP-15/ MT2-MMP, MMP-16/MT3-MMP, MMP-19, MMP-2 (72 kDa Collagenase IV), MMP-23, MMP-7 (Matrilysin), MMP-9 (92 kDa Collagenase IV), Moesin, mRANKL, Muc-1, Mucin 2, Mucin 3 (MUC3), Mucin 5AC, MyD88, Myelin/ Oligodendrocyte, Myeloid Specific Marker, Myeloperoxidase, MyoD1, Myogenin, Myoglobin, Myosin Smooth Muscle Heavy Chain, Nck, Negative Control for Mouse IgG1, Negative Control for Mouse IgG2a, Negative Control for Mouse IgG3, Negative Control for Mouse IgM, Negative Control for Rabbit IgG, Neurofilament, Neurofilament (160 kDa), Neurofilament (200 kDa), Neurofilament (68 kDa), Neuron Specific Enolase, Neutrophil Elastase, NF kappa B/p50, NF kappa B/p65 (Rel A), NGF-Receptor (p75NGFR), brain Nitric Oxide Synthase (bNOS), endothelial Nitric Oxide Synthase (eNOS), nm23, NOS-i, NOS-u, Notch, Nucleophosmin (NPM), NuMA, O ct-1, Oct-2/, Oct-3/, Ornithine Decarboxylase, Osteopontin, p130, p130cas, p14ARF, p15INK4b, p16INK4a, p170, p170/MDR- 1, p18INK4c, p19ARF, p19Skp1, p21WAF1, p27Kip1, p300/CBP, p35nck5a, P504S, p53, p57Kip2 Ab-7, p63 (p53 Family Member), p73, p73a, p73a/b, p95VAV, Parathyroid Hormone, Parathyroid Hormone Receptor Type 1, Parkin, PARP, PARP (Poly ADP-Ribose Polymerase), Pax-5, Paxillin, PCNA, PCTAIRE2, PDGF, PDGFR alpha, PDGFR beta, Pds1, Perforin, PGP9.5, PHAS- I, PHAS-II, Phospho-Ser/Thr/Tyr, Phosphotyrosine, PLAP, Plasma Cell Marker, Plasminogen, PLC gamma 1, PMP-22, Pneumocystis jiroveci, PPAR-gamma, PR3 (Proteinase 3), Presenillin, Progesterone, Progesterone Receptor, Progesterone Receptor (phospho-specific) - Serine 190, Progesterone Receptor (phospho- specific) - Serine 294, Prohibitin, Prolactin, Prolactin Receptor, Prostate Apoptosis Response Protein-4, Prostate Specific Acid Phosphatase, Prostate Specific Antigen, pS2, PSCA, Rabies Virus, RAD1, Rad51, Raf1, Raf-1 (Phospho- specific), RAIDD, Ras, Rad18, Renal Cell Carcinoma, Ret Oncoprotein, Retinoblastoma, Retinoblastoma (Rb) (Phospho-specific Serine608), Retinoic Acid Receptor (b), Retinoid X Receptor (hRXR), Retinol Binding Protein, Rhodopsin (Opsin), ROC, RPA/p32, RPA/p70, Ruv A, Ruv B, Ruv C, 5100, S100A4, S100A6, SHP-1, SIM Ag (SIMA-4D3), SIRP a1, sm, SODD (Silencer of Death Domain), Somatostatin Receptor-I, SRC1 (Steroid Receptor Coactivator-1) Ab-1, SREBP-1 (Sterol Regulatory Element Binding Protein-1), SRF (Serum Response Factor), Stat-1, Stat3, Stat5, Stat5a, Stat5b, Stat6, Streptavidin, Superoxide Dismutase, Surfactant Protein A, Surfactant Protein B, Surfactant Protein B (Pro), Survivin, SV40 Large T Antigen, Syk, Synaptophysin, Synuclein, Synuclein beta, Synuclein pan, TACE (TNF-alpha converting enzyme)/ ADAM17, TAG-72, tau, TdT, Tenascin, Testosterone, TGF beta 3, TGF-beta 2, Thomsen-Friedenreich Antigen, Thrombospondin, Thymidine Phosphorylase, Thymidylate Synthase, Thymine Glycols, Thyroglobulin, Thyroid Hormone Receptor beta, Thyroid Hormone Receptor, Thyroid Stimulating Hormone (TSH), TID-1, TIMP-1, TIMP-2, TNF alpha, TNFa, TNR-R2, Topo II beta, Topoisomerase IIa, Toxoplasma Gondii, TR2, TRADD, Transforming Growth Factor a, Transglutaminase II, TRAP, Tropomyosin, TRP75/gp75, TrxR2, TTF- 1, Tubulin, Tubulin-a, Tubulin-b, Tyrosinase, Ubiquitin, UCP3, uPA, Urocortin, Vacular Endothelial Growth Factor(VEGF), Vimentin, Vinculin, Vitamin D Receptor (VDR), von Hippel-Lindau Protein, Wnt-1, Xanthine Oxidase, XPA, XPF, XPG, XRCC1, XRCC2, ZAP-70, Zip kinase Known Cancer ABL1, ABL2, ACSL3, AF15Q14, AF1Q, AF3p21, AF5q31, AKAP9, AKT1, Genes AKT2, ALDH2, ALK, ALO17, APC, ARHGEF12, ARHH, ARID1A, ARID2, ARNT, ASPSCR1, ASXL1, ATF1, ATIC, ATM, ATRX, BAP1, BCL10, BCL11A, BCL11B, BCL2, BCL3, BCL5, BCL6, BCL7A, BCL9, BCOR, BCR, BHD, BIRC3, BLM, BMPR1A, BRAF, BRCA1, BRCA2, BRD3, BRD4, BRIP1, BTG1, BUB1B, C12orf9, C15orf21, C15orf55, C16orf75, CANT1, CARD11, CARS, CBFA2T1, CBFA2T3, CBFB, CBL, CBLB, CBLC, CCNB1IP1, CCND1, CCND2, CCND3, CCNE1, CD273, CD274, CD74, CD79A, CD79B, CDH1, CDH11, CDK12, CDK4, CDK6, CDKN2A, CDKN2a(p14), CDKN2C, CDX2, CEBPA, CEP1, CHCHD7, CHEK2, CHIC2, CHN1, CIC, CIITA, CLTC, CLTCL1, CMKOR1, COL1A1, COPEB, COX6C, CREB1, CREB3L1, CREB3L2, CREBBP, CRLF2, CRTC3, CTNNB1, CYLD, D10S170, DAXX, DDB2, DDIT3, DDX10, DDX5, DDX6, DEK, DICER1, DNMT3A, DUX4, EBF1, EGFR, EIF4A2, ELF4, ELK4, ELKS, ELL, ELN, EML4, EP300, EPS15, ERBB2, ERCC2, ERCC3, ERCC4, ERCC5, ERG, ETV1, ETV4, ETV5, ETV6, EVI1, EWSR1, EXT1, EXT2, EZH2, FACL6, FAM22A, FAM22B, FAM46C, FANCA, FANCC, FANCD2, FANCE, FANCF, FANCG, FBXO11, FBXW7, FCGR2B, FEV, FGFR1, FGFR1OP, FGFR2, FGFR3, FH, FHIT, FIP1L1, FLI1, FLJ27352, FLT3, FNBP1, FOXL2, FOXO1A, FOXO3A, FOXP1, FSTL3, FUBP1, FUS, FVT1, GAS7, GATA1, GATA2, GATA3, GMPS, GNA11, GNAQ, GNAS, GOLGA5, GOPC, GPC3, GPHN, GRAF, HCMOGT-1, HEAB, HERPUD1, HEY1, HIP1, HIST1H4I, HLF, HLXB9, HMGA1, HMGA2, HNRNPA2B1, HOOK3, HOXA11, HOXA13, HOXA9, HOXC11, HOXC13, HOXD11, HOXD13, HRAS, HRPT2, HSPCA, HSPCB, IDH1, IDH2, IGH@, IGK@, IGL@, IKZF1, IL2, IL21R, IL6ST, IL7R, IRF4, IRTA1, ITK, JAK1, JAK2, JAK3, JAZF1, JUN, KDM5A, KDM5C, KDM6A, KDR, KIAA1549, KIT, KLK2, KRAS, KTN1, LAF4, LASP1, LCK, LCP1, LCX, LHFP, LIFR, LMO1, LMO2, LPP, LYL1, MADH4, MAF, MAFB, MALT1, MAML2, MAP2K4, MDM2, MDM4, MDS1, MDS2, MECT1, MED12, MEN1, MET, MITF, MKL1, MLF1, MLH1, MLL, MLL2, MLL3, MLLT1, MLLT10, MLLT2, MLLT3, MLLT4, MLLT6, MLLT7, MN1, MPL, MSF, MSH2, MSH6, MSI2, MSN, MTCP1, MUC1, MUTYH, MYB, MYC, MYCL1, MYCN, MYD88, MYH11, MYH9, MYST4, NACA, NBS1, NCOA1, NCOA2, NCOA4, NDRG1, NF1, NF2, NFE2L2, NFIB, NFKB2, NIN, NKX2-1, NONO, NOTCH1, NOTCH2, NPM1, NR4A3, NRAS, NSD1, NTRK1, NTRK3, NUMA1, NUP214, NUP98, OLIG2, OMD, P2RY8, PAFAH1B2, PALB2, PAX3, PAX5, PAX7, PAX8, PBRM1, PBX1, PCM1, PCSK7, PDE4DIP, PDGFB, PDGFRA, PDGFRB, PER1, PHOX2B, PICALM, PIK3CA, PIK3R1, PIM1, PLAG1, PML, PMS1, PMS2, PMX1, PNUTL1, POU2AF1, POU5F1, PPARG, PPP2R1A, PRCC, PRDM1, PRDM16, PRF1, PRKAR1A, PRO1073, PSIP2, PTCH, PTEN, PTPN11, RAB5EP, RAD51L1, RAF1, RALGDS, RANBP17, RAP1GDS1, RARA, RB1, RBM15, RECQL4, REL, RET, ROS1, RPL22, RPN1, RUNDC2A, RUNX1, RUNXBP2, SBDS, SDH5, SDHB, SDHC, SDHD, SEPT6, SET, SETD2, SF3B1, SFPQ, SFRS3, SH3GL1, SIL, SLC45A3, SMARCA4, SMARCB1, SMO, SOCS1, SOX2, SRGAP3, SRSF2, SS18, SS18L1, SSH3BP1, SSX1, SSX2, SSX4, STK11, STL, SUFU, SUZ12, SYK, TAF15, TAL1, TAL2, TCEA1, TCF1, TCF12, TCF3, TCF7L2, TCL1A, TCL6, TET2, TFE3, TFEB, TFG, TFPT, TFRC, THRAP3, TIF1, TLX1, TLX3, TMPRSS2, TNFAIP3, TNFRSF14, TNFRSF17, TNFRSF6, TOP1, TP53, TPM3, TPM4, TPR, TRA@, TRB@, TRD@, TRIM27, TRIM33, TRIP11, TSC1, TSC2, TSHR, TTL, U2AF1, USP6, VHL, VTI1A, WAS, WHSC1, WHSC1L1, WIF1, WRN, WT1, WTX, XPA, XPC, XPO1, YWHAE, ZNF145, ZNF198, ZNF278, ZNF331, ZNF384, ZNF521, ZNF9, ZRSR2 Known Cancer AR, androgen receptor; ARPC1A, actin-related protein complex 2/3 subunit A; Genes AURKA, Aurora kinase A; BAG4, BCl-2 associated anthogene 4; BCl2l2, BCl-2 like 2; BIRC2, Baculovirus IAP repeat containing protein 2; CACNA1E, calcium channel voltage dependent alpha-1E subunit; CCNE1, cyclin E1; CDK4, cyclin dependent kinase 4; CHD1L, chromodomain helicase DNA binding domain 1- like; CKS1B, CDC28 protein kinase 1B; COPS3, COP9 subunit 3; DCUN1D1, DCN1 domain containing protein 1; DYRK2, dual specificity tyrosine phosphorylation regulated kinase 2; EEF1A2, eukaryotic elongation transcription factor 1 alpha 2; EGFR, epidermal growth factor receptor; FADD, Fas-associated via death domain; FGFR1, fibroblast growth factor receptor 1, GATA6, GATA binding protein 6; GPC5, glypican 5; GRB7, growth factor receptor bound protein 7; MAP3K5, mitogen activated protein kinase kinase kinase 5; MED29, mediator complex subunit 5; MITF, microphthalmia associated transcription factor; MTDH, metadherin; NCOA3, nuclear receptor coactivator 3; NKX2-1, NK2 homeobox 1; PAK1, p21/CDC42/RAC1-activated kinase 1; PAX9, paired box gene 9; PIK3CA, phosphatidylinositol-3 kinase catalytic a; PLA2G10, phopholipase A2, group X; PPM1D, protein phosphatase magnesium-dependent 1D; PTK6, protein tyrosine kinase 6; PRKCI, protein kinase C iota; RPS6KB1, ribosomal protein s6 kinase 70 kDa; SKP2, s-phase kinase associated protein; SMURF1, sMAD specific E3 ubiquitin protein ligase 1; SHH, sonic hedgehog homologue; STARD3, sTAR- related lipid transfer domain containing protein 3; YWHAQ, tyrosine 3- monooxygenase/tryptophan 5-monooxygenase activation protein, zeta isoform; ZNF217, zinc finger protein 217 Mitotic Related Aurora kinase A (AURKA); Aurora kinase B (AURKB); Baculoviral IAP repeat- Cancer Genes containing 5, survivin (BIRC5); Budding uninhibited by benzimidazoles 1 homolog (BUB1); Budding uninhibited by benzimidazoles 1 homolog beta, BUBR1 (BUB1B); Budding uninhibited by benzimidazoles 3 homolog (BUB3); CDC28 protein kinase regulatory subunit 1B (CKS1B); CDC28 protein kinase regulatory subunit 2 (CKS2); Cell division cycle 2 (CDC2)/CDK1 Cell division cycle 20 homolog (CDC20); Cell division cycle-associated 8, borealin (CDCA8); Centromere protein F, mitosin (CENPF); Centrosomal protein 110 kDa (CEP110); Checkpoint with forkhead and ring finger domains (CHFR); Cyclin B1 (CCNB1); Cyclin B2 (CCNB2); Cytoskeleton-associated protein 5 (CKAP5/ch-TOG); Microtubule-associated protein RP/EB family member 1. End-binding protein 1, EB1 (MAPRE1); Epithelial cell transforming sequence 2 oncogene (ECT2); Extra spindle poles like 1, separase (ESPL1); Forkhead box M1 (FOXM1); H2A histone family, member X (H2AFX); Kinesin family member 4A (KIF4A); Kinetochore- associated 1 (KNTC1/ROD); Kinetochore-associated 2; highly expressed in cancer 1 (KNTC2/HEC1); Large tumor suppressor, homolog 1 (LATS1); Large tumor suppressor, homolog 2 (LATS2); Mitotic arrest deficient-like 1; MAD1 (MAD1L1); Mitotic arrest deficient-like 2; MAD2 (MAD2L1); Mps1 protein kinase (TTK); Never in mitosis gene a-related kinase 2 (NEK2); Ninein, GSK3b interacting protein (NIN); Non-SMC condensin I complex, subunit D2 (NCAPD2/CNAP1); Non-SMC condensin I complex, subunit H (NACPH/CAPH); Nuclear mitotic apparatus protein 1 (NUMA1); Nucleophosmin (nucleolar phosphoprotein B23, numatrin); (NPM1); Nucleoporin (NUP98); Pericentriolar material 1 (PCM1); Pituitary tumor-transforming 1, securin (PTTG1); Polo-like kinase 1 (PLK1); Polo-like kinase 4 (PLK4/SAK); Protein (peptidylprolyl cis/trans isomerase) NIMA-interacting 1 (PIN1); Protein regulator of cytokinesis 1 (PRC1); RAD21 homolog (RAD21); Ras association (RalGDS/AF-6); domain family 1 (RASSF1); Stromal antigen 1 (STAG1); Synuclein-c, breast cancer-specific protein 1 (SNCG, BCSG1); Targeting protein for Xklp2 (TPX2); Transforming, acidic coiled-coil containing protein 3 (TACC3); Ubiquitin-conjugating enzyme E2C (UBE2C); Ubiquitin-conjugating enzyme E2I (UBE2I/UBC9); ZW10 interactor, (ZWINT); ZW10, kinetochore- associated homolog (ZW10); Zwilch, kinetochore-associated homolog (ZWILCH)

Additional non-limiting lists of biomarkers are listed below.

Breast Cancer

Breast cancer specific biomarkers can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNA, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 3.

One or more breast cancer specific biomarker can be assessed to provide a breast cancer specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, including but not limited to, miR-21, miR-155, miR-206, miR-122a, miR-210, miR-21, miR-155, miR-206, miR-122a, miR-210, or miR-21, or any combination thereof.

The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, let-7, miR-10b, miR-125a, miR-125b, miR-145, miR-143, miR-145, miR-16, or any combination thereof.

The mRNAs that may be analyzed can include, but are not limited to, ER, PR, HER2, MUC1, or EGFR, or any combination thereof. Mutations including, but not limited to, those related to KRAS, B-Raf, or CYP2D6, or any combination thereof can also be used as specific biomarkers from a vesicle for breast cancer. In addition, a protein, ligand, or peptide that can be used as biomarkers from a vesicle that is specific to breast cancer includes, but are not limited to, hsp70, MART-1, TRP, HER2, hsp70, MART-1, TRP, HER2, ER, PR, Class III b-tubulin, or VEGFA, or any combination thereof. Furthermore the snoRNA that can be used as an exosomal biomarker for breast cancer include, but are not limited to, GAS5. The gene fusion ETV6-NTRK3 can also be used a biomarker for breast cancer.

The invention also provides an isolated vesicle comprising one or more breast cancer specific biomarkers, such as ETV6-NTRK3, or biomarkers listed in FIG. 3 and in FIG. 1 for breast cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more breast cancer specific biomarkers, such as ETV6-NTRK3, or biomarkers listed in FIG. 3 and in FIG. 1 for breast cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for breast cancer specific vesicles or vesicles comprising one or more breast cancer specific biomarkers, such as ETV6-NTRK3, or biomarkers listed in FIG. 3 and in FIG. 1 for breast cancer.

One or more breast cancer specific biomarkers, such as ETV6-NTRK3, or biomarkers listed in FIG. 3 and in FIG. 1 for breast cancer can also be detected by one or more systems disclosed herein, for characterizing a breast cancer. For example, a detection system can comprise one or more probes to detect one or more breast cancer specific biomarkers, such as ETV6-NTRK3, or biomarkers listed in FIG. 3 and in FIG. 1 for breast cancer, of one or more vesicles of a biological sample.

Biomarkers that are used in methods of the invention to assess breast cancer include without limitation BCA-225, hsp70, MART1, ER, VEGFA, Class III b-tubulin, HER2/neu (e.g., for Her2+ breast cancer), GPR30, ErbB4 (JM) isoform, MPR8, MISIIR, CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, a progesterone receptor (PR) or its isoform (PR(A) or PR(B)), P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2, Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR, or any combination thereof. One or more antigens CD9, MIS Rii, ER, CD63, MUC1, HER3, STAT3, VEGFA, BCA, CA125, CD24, EPCAM, and ERB B4 can be used to assess vesicles derived from breast cancer cells.

One subset for assessing vesicles comprises CD10, NPGP/NPFF2, HER2/ERBB2, AGTR1, NPY1R, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1 or a combination thereof.

Another subset comprises SPB, SPC, NSE, PGP9.5, CD9, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, EGFR, B7H3, IC3b, MUC1, mesothelin, SPA, PCSA, CD63, STEAP, AQP5, CD81, DR3, PSM, GPCR, EphA2, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, A33, CD24, CD10, NGAL, EpCam, MUC17, TROP-2, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR, or a combination thereof.

Yet another subset comprises BRCA, MUC-1, MUC 16, CD24, ErbB4, ErbB2 (HER2), ErbB3, HSP70, Mammaglobin, PR, PR(B), VEGFA, or a combination thereof.

Ovarian Cancer

Ovarian cancer specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 4, and can be used to create a ovarian cancer specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-200a, miR-141, miR-200c, miR-200b, miR-21, miR-141, miR-200a, miR-200b, miR-200c, miR-203, miR-205, miR-214, miR-199*, or miR-215, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-199a, miR-140, miR-145, miR-100, miR-let-7 cluster, or miR-125b-1, or any combination thereof. The one or more mRNAs that may be analyzed can include without limitation ERCC1, ER, TOPO1, TOP2A, AR, PTEN, HER2/neu, CD24 or EGFR, or any combination thereof.

A biomarker mutation for ovarian cancer that can be assessed in a vesicle includes, but is not limited to, a mutation of KRAS, mutation of B-Raf, or any combination of mutations specific for ovarian cancer. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, VEGFA, VEGFR2, or HER2, or any combination thereof. Furthermore, a vesicle isolated or assayed can be ovarian cancer cell specific, or derived from ovarian cancer cells.

The invention also provides an isolated vesicle comprising one or more ovarian cancer specific biomarkers, such as CD24, those listed in FIG. 4 and in FIG. 1 for ovarian cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more ovarian cancer specific biomarkers, such as CD24, those listed in FIG. 4 and in FIG. 1 for ovarian cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for ovarian cancer specific vesicles or vesicles comprising one or more ovarian cancer specific biomarkers, such as CD24, those listed in FIG. 4 and in FIG. 1 for ovarian cancer.

One or more ovarian cancer specific biomarkers, such as CD24, those listed in FIG. 4 and in FIG. 1 for ovarian cancer can also be detected by one or more systems disclosed herein, for characterizing an ovarian cancer. For example, a detection system can comprise one or more probes to detect one or more ovarian cancer specific biomarkers, such as CD24, those listed in FIG. 4 and in FIG. 1 for ovarian cancer, of one or more vesicles of a biological sample.

Lung Cancer

Lung cancer specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 5, and can be used to create a lung cancer specific biosignature.

The biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-21, miR-205, miR-221 (protective), let-7a (protective), miR-137 (risky), miR-372 (risky), or miR-122a (risky), or any combination thereof. The biosignature can comprise one or more upregulated or overexpressed miRNAs, such as miR-17-92, miR-19a, miR-21, miR-92, miR-155, miR-191, miR-205 or miR-210; one or more downregulated or underexpressed miRNAs, such as miR-let-7, or any combination thereof. The one or more biomarker may be miR-92a-2*, miR-147, miR-574-5p, such as for small cell lung cancer.

The one or more mRNAs that may be analyzed can include, but are not limited to, EGFR, PTEN, RRM1, RRM2, ABCB1, ABCG2, LRP, VEGFR2, VEGFR3, class III b-tubulin, or any combination thereof.

A biomarker mutation for lung cancer that can be assessed in a vesicle includes, but is not limited to, a mutation of EGFR, KRAS, B-Raf, UGT1A1, or any combination of mutations specific for lung cancer. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, KRAS, hENT1, or any combination thereof.

The biomarker can also be midkine (MK or MDK). In some embodiments, the lung cancer specific vesicle comprises one or more of SPB, SPC, PSP9.5, NDUFB7, gal3-b2c10, iC3b, MUC1, GPCR, CABYR and muc17, which can be overexpressed in lung cancer samples compared to normals. Furthermore, a vesicle isolated or assayed can be lung cancer cell specific, or derived from lung cancer cells.

The invention also provides an isolated vesicle comprising one or more lung cancer specific biomarkers, such as RLF-MYCL1, TGF-ALK, or CD74-ROS1, or those listed in FIG. 5 and in FIG. 1 for lung cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more lung cancer specific biomarkers, such as RLF-MYCL1, TGF-ALK, or CD74-ROS1, or those listed in FIG. 5 and in FIG. 1 for lung cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for lung cancer specific vesicles or vesicles comprising one or more lung cancer specific biomarkers, such as RLF-MYCL1, TGF-ALK, or CD74-ROS1, or those listed in FIG. 5 and in FIG. 1 for lung cancer. In some embodiments, the lung cancer specific vesicle comprises one or more of SPB, SPC, PSP9.5, NDUFB7, ga13-b2c10, iC3b, MUC1, GPCR, CABYR and muc17.

One or more lung cancer specific biomarkers, such as RLF-MYCL1, TGF-ALK, or CD74-ROS1, or those listed in FIG. 5 and in FIG. 1 for lung cancer can also be detected by one or more systems disclosed herein, for characterizing a lung cancer. For example, a detection system can comprise one or more probes to detect one or more lung cancer specific biomarkers, such as RLF-MYCL1, TGF-ALK, or CD74-ROS1, or those listed in FIG. 5 and in FIG. 1 for lung cancer, of one or more vesicles of a biological sample.

Colon Cancer

Colon cancer specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 6, and can be used to create a colon cancer specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-24-1, miR-29b-2, miR-20a, miR-10a, miR-32, miR-203, miR-106a, miR-17-5p, miR-30c, miR-223, miR-126, miR-128b, miR-21, miR-24-2, miR-99b, miR-155, miR-213, miR-150, miR-107, miR-191, miR-221, miR-20a, miR-510, miR-92, miR-513, miR-19a, miR-21, miR-20, miR-183, miR-96, miR-135b, miR-31, miR-21, miR-92, miR-222, miR-181b, miR-210, miR-20a, miR-106a, miR-93, miR-335, miR-338, miR-133b, miR-346, miR-106b, miR-153a, miR-219, miR-34a, miR-99b, miR-185, miR-223, miR-211, miR-135a, miR-127, miR-203, miR-212, miR-95, or miR-17-5p, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such as miR-143, miR-145, miR-143, miR-126, miR-34b, miR-34c, let-7, miR-9-3, miR-34a, miR-145, miR-455, miR-484, miR-101, miR-145, miR-133b, miR-129, miR-124a, miR-30-3p, miR-328, miR-106a, miR-17-5p, miR-342, miR-192, miR-1, miR-34b, miR-215, miR-192, miR-301, miR-324-5p, miR-30a-3p, miR-34c, miR-331, miR-548c-5p, miR-362-3p, miR-422a, or miR-148b, or any combination thereof.

The one or more biomarker can be an upregulated or overexpressed miRNA, such as miR-20a, miR-21, miR-106a, miR-181b or miR-203, for characterizing a colon adenocarcinoma. The one or more biomarker can be used to characterize a colorectal cancer, such as an upregulated or overexpressed miRNA selected from the group consisting of: miR-19a, miR-21, miR-127, miR-31, miR-96, miR-135b and miR-183, a downregulated or underexpressed miRNA, such as miR-30c, miR-133a, mir143, miR-133b or miR-145, or any combination thereof. The one or more biomarker can be used to characterize a colorectal cancer, such as an upregulated or overexpressed miRNA selected from the group consisting of: miR-548c-5p, miR-362-3p, miR-422a, miR-597, miR-429, miR-200a, and miR-200b, or any combination thereof.

The one or more mRNAs that may be analyzed can include, but are not limited to, EFNB1, ERCC1, HER2, VEGF, or EGFR, or any combination thereof. A biomarker mutation for colon cancer that can be assessed in a vesicle includes, but is not limited to, a mutation of EGFR, KRAS, VEGFA, B-Raf, APC, or p53, or any combination of mutations specific for colon cancer. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, AFRs, Rabs, ADAM10, CD44, NG2, ephrin-B1, MIF, b-catenin, Junction, plakoglobin, glalectin-4, RACK1, tetrspanin-8, FasL, TRAIL, A33, CEA, EGFR, dipeptidase 1, hsc-70, tetraspanins, ESCRT, TS, PTEN, or TOPO1, or any combination thereof. Furthermore, a vesicle isolated or assayed can be colon cancer cell specific, or derived from colon cancer cells.

The invention also provides an isolated vesicle comprising one or more colon cancer specific biomarkers, such as listed in FIG. 6 and in FIG. 1 for colon cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more colon cancer specific biomarkers, such as listed in FIG. 6 and in FIG. 1 for colon cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for colon cancer specific vesicles or vesicles comprising one or more colon cancer specific biomarkers, such as listed in FIG. 6 and in FIG. 1 for colon cancer.

One or more colon cancer specific biomarkers, such as listed in FIG. 6 and in FIG. 1 for colon cancer can also be detected by one or more systems disclosed herein, for characterizing a colon cancer. For example, a detection system can comprise one or more probes to detect one or more colon cancer specific biomarkers, such as listed in FIG. 6 and in FIG. 1 for colon cancer, of one or more vesicles of a biological sample.

Adenoma Versus Hyperplastic Polyp

Adenoma versus hyperplastic polyp specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, or any combination thereof, such as listed in FIG. 7, and can be used to create an adenoma versus hyperplastic polyp specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, ABCA8, KIAA1199, GCG, MAMDC2, C2orf32, 229670_at, IGF1, PCDH7, PRDX6, PCNA, COX2, or MUC6, or any combination thereof.

A biomarker mutation to distinguish for adenoma versus hyperplastic polyp that can be assessed in a vesicle includes, but is not limited to, a mutation of KRAS, mutation of B-Raf, or any combination of mutations specific for distinguishing between adenoma versus hyperplastic polyp. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, hTERT.

The invention also provides an isolated vesicle comprising one or more specific biomarkers for distinguishing between an adenoma and a hyperplastic polyp, such as listed in FIG. 7. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more specific biomarkers for distinguishing between an adenoma and a hyperplastic polyp, such as listed in FIG. 7. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for having one or more specific biomarkers for distinguishing between an adenoma and a hyperplastic polyp, such as listed in FIG. 7.

One or more specific biomarkers for distinguishing between an adenoma and a hyperplastic polyp, such as listed in FIG. 7 can also be detected by one or more systems disclosed herein, for distinguishing between an adenoma and a hyperplastic polyp. For example, a detection system can comprise one or more probes to detect one or more specific biomarkers for distinguishing between an adenoma and a hyperplastic polyp, such as listed in FIG. 7, of one or more vesicles of a biological sample.

Bladder Cancer

Biomarkers for bladder cancer can be used to assess a bladder cancer according to the methods of the invention. The biomarkers can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof. Biomarkers for bladder cancer include without limitation one or more of miR-223, miR-26b, miR-221, miR-103-1, miR-185, miR-23b, miR-203, miR-17-5p, miR-23a, miR-205 or any combination thereof. Further biomarkers for bladder cancer include FGFR3, EGFR, pRB (retinoblastoma protein), 5T4, p53, Ki-67, VEGF, CK20, COX2, p21, Cyclin D1, p14, p15, p16, Her-2, MAPK (mitogen-activated protein kinase), Bax/Bcl-2, PI3K (phosphoinositide-3-kinase), CDKs (cyclin-dependent kinases), CD40, TSP-1, HA-ase, telomerase, survivin, NMP22, TNF, Cyclin E1, p2′7, caspase, survivin, NMP22 (Nuclear matrix protein 22), BCLA-4, Cytokeratins (8, 18, 19 and 20), CYFRA 21-1, IL-2, and complement factor H-related protein. In an embodiment, non-receptor tyrosine kinase ETK/BMX and/or Carbonic Anhydrase IX is used as a marker of bladder cancer for diagnostic, prognostic and therapeutic purposes. See Guo et al., Tyrosine Kinase ETK/BMX Is Up-Regulated in Bladder Cancer and Predicts Poor Prognosis in Patients with Cystectomy. PLoS One. 2011 Mar. 7; 6(3):e17778.; Klatte et al., Carbonic anhydrase IX in bladder cancer: a diagnostic, prognostic, and therapeutic molecular marker. Cancer. 2009 Apr. 1; 115(7):1448-58. The biomarker can be one or more vesicle biomarker associated with bladder cancer as described in Pisitkun et al., Discovery of urinary biomarkers. Mol Cell Proteomics. 2006 October; 5(10):1760-71; Welton et al, Proteomics analysis of bladder cancer exosomes. Mol Cell Proteomics. 2010 June; 9(6):1324-38. These biomarkers can be used for assessing a bladder cancer. The markers can be associated with a vesicle or vesicle population.

Irritable Bowel Disease (IBD)

IBD versus normal biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 8, and can be used to create a IBD versus normal specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, REG1A, MMP3, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more specific biomarkers for distinguishing between IBD and a normal sample, such as listed in FIG. 8. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more specific biomarkers for distinguishing between IBD and a normal sample, such as listed in FIG. 8. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for having one or more specific biomarkers for distinguishing between IBD and a normal sample, such as listed in FIG. 8.

One or more specific biomarkers for distinguishing between IBD and a normal sample, such as listed in FIG. 8 can also be detected by one or more systems disclosed herein, for distinguishing between IBD and a normal sample. For example, a detection system can comprise one or more probes to detect one or more specific biomarkers for distinguishing between IBD and a normal sample, such as listed in FIG. 8, of one or more vesicles of a biological sample.

Adenoma Versus Colorectal Cancer (CRC)

Adenoma versus CRC specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 9, and can be used to create a Adenoma versus CRC specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, GREM1, DDR2, GUCY1A3, TNS1, ADAMTS1, FBLN1, FLJ38028, RDX, FAM129A, ASPN, FRMD6, MCC, RBMS1, SNAI2, MEIS1, DOCK10, PLEKHC1, FAM126A, TBC1D9, VWF, DCN, ROBO1, MSRB3, LATS2, MEF2C, IGFBP3, GNB4, RCN3, AKAP12, RFTN1, 226834_at, COL5A1, GNG2, NR3C1*, SPARCL1, MAB21L2, AXIN2, 236894_at, AEBP1, AP1S2, C10orf56, LPHN2, AKT3, FRMD6, COL15A1, CRYAB, COL14A1, LOC286167, QKI, WWTR1, GNG11, PAPPA, or ELDT1, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more specific biomarkers for distinguishing between an adenoma and a CRC, such as listed in FIG. 9. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more specific biomarkers for distinguishing between an adenoma and a CRC, such as listed in FIG. 9. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for having one or more specific biomarkers for distinguishing between an adenoma and a CRC, such as listed in FIG. 9.

One or more specific biomarkers for distinguishing between an adenoma and a CRC, such as listed in FIG. 9 can also be detected by one or more systems disclosed herein, for distinguishing between an adenoma and a CRC. For example, a detection system can comprise one or more probes to detect one or more specific biomarkers for distinguishing between an adenoma and a CRC, such as listed in FIG. 9, of one or more vesicles of a biological sample.

IBD Versus CRC

IBD versus CRC specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 10, and can be used to create a IBD versus CRC specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, 227458_at, INDO, CXCL9, CCR2, CD38, RARRES3, CXCL10, FAM26F, TNIP3, NOS2A, CCRL1, TLR8, IL18BP, FCRL5, SAMD9L, ECGF1, TNFSF13B, GBP5, or GBP1, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more specific biomarkers for distinguishing between IBD and a CRC, such as listed in FIG. 10. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more specific biomarkers for distinguishing between IBD and a CRC, such as listed in FIG. 10. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for having one or more specific biomarkers for distinguishing between IBD and a CRC, such as listed in FIG. 10.

One or more specific biomarkers for distinguishing between IBD and a CRC, such as listed in FIG. 10 can also be detected by one or more systems disclosed herein, for distinguishing between IBD and a CRC. For example, a detection system can comprise one or more probes to detect one or more specific biomarkers for distinguishing between IBD and a CRC, such as listed in FIG. 10, of one or more vesicles of a biological sample.

CRC Dukes B Versus Dukes C-D

CRC Dukes B versus Dukes C-D specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 11, and can be used to create a CRC D-B versus C-D specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, TMEM37*, IL33, CA4, CCDC58, CLIC6, VERSUSNL1, ESPN, APCDD1, C13orf18, CYP4X1, ATP2A3, LOC646627, MUPCDH, ANPEP, C1orf115, HSD3B2, GBA3, GABRB2, GYLTL1B, LYZ, SPC25, CDKN2B, FAM89A, MOGAT2, SEMA6D, 229376_at, TSPAN5, IL6R, or SLC26A2, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more specific biomarkers for distinguishing between CRC Dukes B and a CRC Dukes C-D, such as listed in FIG. 11. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more specific biomarkers for distinguishing between CRC Dukes B and a CRC Dukes C-D, such as listed in FIG. 11. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for having one or more specific biomarkers for distinguishing between CRC Dukes B and a CRC Dukes C-D, such as listed in FIG. 11.

One or more specific biomarkers for distinguishing between CRC Dukes B and a CRC Dukes C-D, such as listed in FIG. 11 can also be detected by one or more systems disclosed herein, for distinguishing between CRC Dukes B and a CRC Dukes C-D. For example, a detection system can comprise one or more probes to detect one or more specific biomarkers for distinguishing between CRC Dukes B and a CRC Dukes C-D, such as listed in FIG. 11, of one or more vesicles of a biological sample.

Adenoma with Low Grade Dysplasia Versus Adenoma with High Grade Dysplasia

Adenoma with low grade dysplasia versus adenoma with high grade dysplasia specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 12, and can be used to create an adenoma low grade dysplasia versus adenoma high grade dysplasia specific biosignature. For example, the one or mRNAs that may be analyzed can include, but are not limited to, SI, DMBT1, CFI*, AQP1, APOD, TNFRSF17, CXCL10, CTSE, IGHA1, SLC9A3, SLC7A1, BATF2, SOCS1, DOCK2, NOS2A, HK2, CXCL2, IL15RA, POU2AF1, CLEC3B, ANI3BP, MGC13057, LCK*, C4BPA, HOXC6, GOLT1A, C2orf32, IL10RA, 240856_at, SOCS3, MEIS3P1, HIPK1, GLS, CPLX1, 236045_x_at, GALC, AMN, CCDC69, CCL28, CPA3, TRIB2, HMGA2, PLCL2, NR3C1, EIF5A, LARP4, RP5-1022P6.2, PHLDB2, FKBP1B, INDO, CLDN8, CNTN3, PBEF1, SLC16A9, CDC25B, TPSB2, PBEF1, ID4, GJB5, CHN2, LIMCH1, or CXCL9, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more specific biomarkers for distinguishing between adenoma with low grade dysplasia and adenoma with high grade dysplasia, such as listed in FIG. 12. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more specific biomarkers for distinguishing between adenoma with low grade dysplasia and adenoma with high grade dysplasia, such as listed in FIG. 12. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for having one or more specific biomarkers for distinguishing between adenoma with low grade dysplasia and adenoma with high grade dysplasia, such as listed in FIG. 12.

One or more specific biomarkers for distinguishing between adenoma with low grade dysplasia and adenoma with high grade dysplasia, such as listed in FIG. 12 can also be detected by one or more systems disclosed herein, for distinguishing between adenoma with low grade dysplasia and adenoma with high grade dysplasia. For example, a detection system can comprise one or more probes to detect one or more specific biomarkers for distinguishing between adenoma with low grade dysplasia and adenoma with high grade dysplasia, such as listed in FIG. 12, of one or more vesicles of a biological sample.

Ulcerative Colitis (UC) Versus Crohn's Disease (CD)

Ulcerative colitis (UC) versus Crohn's disease (CD) specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 13, and can be used to create a UC versus CD specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, IFITM1, IFITM3, STAT1, STAT3, TAP1, PSME2, PSMB8, HNF4G, KLF5, AQP8, APT2B1, SLC16A, MFAP4, CCNG2, SLC44A4, DDAH1, TOB1, 231152_at, MKNK1, CEACAM7*, 1562836_at, CDC42SE2, PSD3, 231169_at, IGL@*, GSN, GPM6B, CDV3*, PDPK1, ANP32E, ADAM9, CDH1, NLRP2, 215777_at, OSBPL1, VNN1, RABGAP1L, PHACTR2, ASH1L, 213710_s_at, CDH1, NLRP2, 215777_at, OSBPL1, VNN1, RABGAP1L, PHACTR2, ASH1, 213710_s_at, ZNF3, FUT2, IGHA1, EDEM1, GPR171, 229713_at, LOC643187, FLVCR1, SNAP23*, ETNK1, LOC728411, POSTN, MUC12, HOXA5, SIGLEC1, LARP5, PIGR, SPTBN1, UFM1, C6orf62, WDR90, ALDH1A3, F2RL1, IGHV1-69, DUOX2, RAB5A, or CP, or any combination thereof can also be used as specific biomarkers from a vesicle for UC versus CD.

A biomarker mutation for distinguishing UC versus CD that can be assessed in a vesicle includes, but is not limited to, a mutation of CARD15, or any combination of mutations specific for distinguishing UC versus CD. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, (P) ASCA.

The invention also provides an isolated vesicle comprising one or more specific biomarkers for distinguishing between UC and CD, such as listed in FIG. 13. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more specific biomarkers for distinguishing between UC and CD, such as listed in FIG. 13. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for having one or more specific biomarkers for distinguishing between UC and CD, such as listed in FIG. 13.

One or more specific biomarkers for distinguishing between UC and CD, such as listed in FIG. 13 can also be detected by one or more systems disclosed herein, for distinguishing between UC and CD. For example, a detection system can comprise one or more probes to detect one or more specific biomarkers for distinguishing between UC and CD, such as listed in FIG. 13, of one or more vesicles of a biological sample.

Hyperplastic Polyp

Hyperplastic polyp versus normal specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 14, and can be used to create a hyperplastic polyp versus normal specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, SLC6A14, ARHGEF10, ALS2, IL1RN, SPRY4, PTGER3, TRIM29, SERPINB5, 1560327_(—)4 ZAK, BAG4, TRIB3, TTL, FOXQ1, or any combination.

The invention also provides an isolated vesicle comprising one or more hyperplastic polyp specific biomarkers, such as listed in FIG. 14. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more hyperplastic polyp specific biomarkers, such as listed in FIG. 14. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for hyperplastic polyp specific vesicles or vesicles comprising one or more hyperplastic polyp specific biomarkers, such as listed in FIG. 14.

One or more hyperplastic polyp specific biomarkers, such as listed in FIG. 14 can also be detected by one or more systems disclosed herein, for characterizing a hyperplastic polyp. For example, a detection system can comprise one or more probes to detect one or more listed in FIG. 14. One or more hyperplastic specific biomarkers, such as listed in FIG. 14, of one or more vesicles of a biological sample.

Adenoma with Low Grade Dysplasia Versus Normal

Adenoma with low grade dysplasia versus normal specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 15, and can be used to create an adenoma low grade dysplasia versus normal specific biosignature. For example, the RNAs that may be analyzed can include, but are not limited to, UGT2A3, KLK11, KIAA1199, FOXQ1, CLDN8, ABCA8, or PYY, or any combination thereof and can be used as specific biomarkers from a vesicle for Adenoma low grade dysplasia versus normal. Furthermore, the snoRNA that can be used as an exosomal biomarker for adenoma low grade dysplasia versus normal can include, but is not limited to, GAS5.

The invention also provides an isolated vesicle comprising one or more specific biomarkers for distinguishing between adenoma with low grade dysplasia and normal, such as listed in FIG. 15. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more specific biomarkers for distinguishing between adenoma with low grade dysplasia and normal, such as listed in FIG. 15. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for having one or more specific biomarkers for distinguishing between adenoma with low grade dysplasia and normal, such as listed in FIG. 15.

One or more specific biomarkers for distinguishing between adenoma with low grade dysplasia and normal, such as listed in FIG. 15 can also be detected by one or more systems disclosed herein, for distinguishing between adenoma with low grade dysplasia and normal. For example, a detection system can comprise one or more probes to detect one or more specific biomarkers for distinguishing between adenoma with low grade dysplasia and normal, such as listed in FIG. 15, of one or more vesicles of a biological sample.

Adenoma Versus Normal

Adenoma versus normal specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 16, and can be used to create an Adenoma versus normal specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, KIAA1199, FOXQ1, or CA7, or any combination thereof. The protein, ligand, or peptide that can be used as a biomarker from a vesicle that is specific to adenoma versus. normal can include, but is not limited to, Clusterin.

The invention also provides an isolated vesicle comprising one or more specific biomarkers for distinguishing between adenoma and normal, such as listed in FIG. 16. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more specific biomarkers for distinguishing between adenoma and normal, such as listed in FIG. 16. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for having one or more specific biomarkers for distinguishing between adenoma and normal, such as listed in FIG. 16.

One or more specific biomarkers for distinguishing between adenoma and normal, such as listed in FIG. 16 can also be detected by one or more systems disclosed herein, for distinguishing between adenoma and normal. For example, a detection system can comprise one or more probes to detect one or more specific biomarkers for distinguishing between adenoma and normal, such as listed in FIG. 16, of one or more vesicles of a biological sample.

CRC Versus Normal

CRC versus normal specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 17, and can be used to create a CRC versus normal specific biosignature. For example, the one or mRNAs that may be analyzed can include, but are not limited to, VWF, IL8, CHI3L1, S100A8, GREM1, or ODC, or any combination thereof and can be used as specific biomarkers from a vesicle for CRC versus normal.

A biomarker mutation for CRC versus normal that can be assessed in a vesicle includes, but is not limited to, a mutation of KRAS, BRAF, APC, MSH2, or MLH1, or any combination of mutations specific for distinguishing between CRC versus normal. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, cytokeratin 13, calcineurin, CHK1, clathrin light chain, phospho-ERK, phospho-PTK2, or MDM2, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more specific biomarkers for distinguishing between CRC and normal, such as listed in FIG. 17. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more specific biomarkers for distinguishing between CRC and normal, such as listed in FIG. 17. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for having one or more specific biomarkers for distinguishing between CRC and normal, such as listed in FIG. 17.

One or more specific biomarkers for distinguishing between CRC and normal, such as listed in FIG. 17 can also be detected by one or more systems disclosed herein, for distinguishing between CRC and normal. For example, a detection system can comprise one or more probes to detect one or more specific biomarkers for distinguishing between CRC and normal, such as listed in FIG. 17, of one or more vesicles of a biological sample.

Benign Prostatic Hyperplasia (BPH)

Benign prostatic hyperplasia (BPH) specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 18, and can be used to create a BPH specific biosignature. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, intact fibronectin.

The invention also provides an isolated vesicle comprising one or more BPH specific biomarkers, such as listed in FIG. 18 and in FIG. 1 for BPH. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more BPH specific biomarkers, such as listed in FIG. 18 and in FIG. 1 for BPH. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for BPH specific vesicles or vesicles comprising one or more BPH specific biomarkers, such as listed in FIG. 18 and in FIG. 1 for BPH.

One or more BPH specific biomarkers, such as listed in FIG. 18 and in FIG. 1 for BPH, can also be detected by one or more systems disclosed herein, for characterizing a BPH. For example, a detection system can comprise one or more probes to detect one or more BPH specific biomarkers, such as listed in FIG. 18 and in FIG. 1 for BPH, of one or more vesicles of a biological sample.

Prostate Cancer

Prostate cancer specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 19, and can be used to create a prostate cancer specific biosignature. For example, a biosignature for prostate cancer can comprise miR-9, miR-21, miR-141, miR-370, miR-200b, miR-210, miR-155, or miR-196a. In some embodiments, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-202, miR-210, miR-296, miR-320, miR-370, miR-373, miR-498, miR-503, miR-184, miR-198, miR-302c, miR-345, miR-491, miR-513, miR-32, miR-182, miR-31, miR-26a-1/2, miR-200c, miR-375, miR-196a-1/2, miR-370, miR-425, miR-425, miR-194-1/2, miR-181a-1/2, miR-34b, let-71, miR-188, miR-25, miR-106b, miR-449, miR-99b, miR-93, miR-92-1/2, miR-125a, miR-141, miR-29a, miR-145 or any combination thereof. In some embodiments, the biosignature comprises one or more miRs overexpressed in prostate cancer including miR-29a and/or miR-145. In some embodiments, the biosignature comprises one or more miRs overexpressed in prostate cancer including hsa-miR-1974, hsa-miR-27b, hsa-miR-103, hsa-miR-146a, hsa-miR-22, hsa-miR-382, hsa-miR-23a, hsa-miR-376c, hsa-miR-335, hsa-miR-142-5p, hsa-miR-221, hsa-miR-142-3p, hsa-miR-151-3p and hsa-miR-21, or miR-141, or any combination thereof.

The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, let-7a, let-7b, let-7c, let-7d, let-7g, miR-16, miR-23a, miR-23b, miR-26a, miR-92, miR-99a, miR-103, miR-125a, miR-125b, miR-143, miR-145, miR-195, miR-199, miR-221, miR-222, miR-497, let-7f, miR-19b, miR-22, miR-26b, miR-27a, miR-27b, miR-29a, miR-29b, miR-305p, miR-30c, miR-100, miR-141, miR-148a, miR-205, miR-520h, miR-494, miR-490, miR-133a-1, miR-1-2, miR-218-2, miR-220, miR-128a, miR-221, miR-499, miR-329, miR-340, miR-345, miR-410, miR-126, miR-205, miR-7-1/2, miR-145, miR-34a, miR-487, or let-7b, or any combination thereof. The biosignature can comprise upregulated or overexpressed miR-21, downregulated or underexpressed miR-15a, miR-16-1, miR-143 or miR-145, or any combination thereof.

The one or more mRNAs that may be analyzed can include, but are not limited to, AR, PCA3, or any combination thereof and can be used as specific biomarkers from a vesicle for prostate cancer.

The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, FASLG or HSP60, PSMA, PCSA or TNFSF10 or any combination thereof. Antibodies for binding PSMA are found in U.S. Pat. Nos. 6,207,805 and 6,512,096, which are incorporated herein by reference in their entirety. Furthermore, a vesicle isolated or assayed can be prostate cancer cell specific, or derived from prostate cancer cells. Furthermore, the snoRNA that can be used as an exosomal biomarker for prostate cancer can include, but is not limited to, U50. Examples of prostate cancer biosignatures are further described below.

The invention also provides an isolated vesicle comprising one or more prostate cancer specific biomarkers, such as ACSL3-ETV1, C15ORF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4, or those listed in FIGS. 19, 60 and in FIG. 1 for prostate cancer. In some embodiments, the isolated vesicle is EpCam+, CK+, CD45−. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more prostate cancer specific biomarkers such as ACSL3-ETV1, C150RF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4, or those listed in FIGS. 19, 60 and in FIG. 1 for prostate cancer. In some embodiments, the composition comprises a population of vesicles that are EpCam+, CK+, CD45−. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for prostate cancer specific vesicles or vesicles comprising one or more prostate cancer specific biomarkers, such as ACSL3-ETV1, C150RF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4, or those listed in FIGS. 19, 60 and in FIG. 1 for prostate cancer. In one embodiment, the composition can comprise a substantially enriched population of vesicles that are EpCam+, CK+, CD45−.

One or more prostate cancer specific biomarkers, such as ACSL3-ETV1, C150RF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4, or those listed in FIGS. 19, 60 and in FIG. 1 for prostate cancer can also be detected by one or more systems disclosed herein, for characterizing a prostate cancer. In some embodiments, the biomarkers EpCam, CK (cytokeratin), and CD45 are detected by one or more of systems disclosed herein, for characterizing prostate cancer, such as determining the prognosis for a subject's prostate cancer, or the therapy-resistance of a subject. For example, a detection system can comprise one or more probes to detect one or more prostate cancer specific biomarkers, such as ACSL3-ETV1, C150RF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4, or those listed in FIGS. 19, 60 and in FIG. 1 for prostate cancer, of one or more vesicles of a biological sample. In one embodiment, the detection system can comprise one or more probes to detect EpCam, CK, CD45, or a combination thereof.

Melanoma

Melanoma specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 20, and can be used to create a melanoma specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-19a, miR-144, miR-200c, miR-211, miR-324-5p, miR-331, or miR-374, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-9, miR-15a, miR-17-3p, miR-23b, miR-27a, miR-28, miR-29b, miR-30b, miR-31, miR-34b, miR-34c, miR-95, miR-96, miR-100, miR-104, miR-105, miR-106a, miR-107, miR-122a, miR-124a, miR-125b, miR-127, miR-128a, miR-128b, miR-129, miR-135a, miR-135b, miR-137, miR-138, miR-139, miR-140, miR-141, miR-145, miR-149, miR-154, miR-154#3, miR-181a, miR-182, miR-183, miR-184, miR-185, miR-189, miR-190, miR-199, miR-199b, miR-200a, miR-200b, miR-204, miR-213, miR-215, miR-216, miR-219, miR-222, miR-224, miR-299, miR-302a, miR-302b, miR-302c, miR-302d, miR-323, miR-325, let-7a, let-7b, let-7d, let-7e, or let-7g, or any combination thereof.

The one or more mRNAs that may be analyzed can include, but are not limited to, MUM-1, beta-catenin, or Nop/5/Sik, or any combination thereof and can be used as specific biomarkers from a vesicle for melanoma.

A biomarker mutation for melanoma that can be assessed in a vesicle includes, but is not limited to, a mutation of CDK4 or any combination of mutations specific for melanoma. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, DUSP-1, Alix, hsp70, Gib2, Gia, moesin, GAPDH, malate dehydrogenase, p120 catenin, PGRL, syntaxin-binding protein 1 & 2, septin-2, or WD-repeat containing protein 1, or any combination thereof. The snoRNA that can be used as an exosomal biomarker for melanoma include, but are not limited to, H/ACA (U107f), SNORA11D, or any combination thereof. Furthermore, a vesicle isolated or assayed can be melanoma cell specific, or derived from melanoma cells.

The invention also provides an isolated vesicle comprising one or more melanoma specific biomarkers, such as listed in FIG. 20 and in FIG. 1 for melanoma. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more melanoma specific biomarkers, such as listed in FIG. 20 and in FIG. 1 for melanoma. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for melanoma specific vesicles or vesicles comprising one or more melanoma specific biomarkers, such as listed in FIG. 20 and in FIG. 1 for melanoma.

One or more melanoma specific biomarkers, such as listed in FIG. 20 and in FIG. 1 for melanoma can also be detected by one or more systems disclosed herein, for characterizing a melanoma. For example, a detection system can comprise one or more probes to detect one or more cancer specific biomarkers, such as listed in FIG. 20 and in FIG. 1 for melanoma, of one or more vesicles of a biological sample.

Biomarkers associated with melanoma microvesicles include HSPA8, CD63, ACTB, GAPDH, ANXA2, CD81, ENO1, PDCD6IP, SDCBP, EZR, MSN, YWHAE, ACTG1, ANXA6, LAMP2, TPI1, ANXA5, GDI2, GSTP1, HSPA1A, HSPA1B, LDHB, LAMP1, EEF2, RAB5B, RDX, GNB1, KRT10, MDH1, STXBP2, RAN, ACLY, CAPZB, GNA11, IGSF8, WDR1, CAV1, CTNND1, PGAM1, AKR1B1, EGFR, MLANA, MCAM, PPP1CA, STXBP1, TGFB1, SEPT2, and TSNAXIP1. One or more of these markers can be assessed to characterize a melanoma.

Pancreatic Cancer

Pancreatic cancer specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 21, and can be used to create a pancreatic cancer specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-221, miR-181a, miR-155, miR-210, miR-213, miR-181b, miR-222, miR-181b-2, miR-21, miR-181b-1, miR-220, miR-181d, miR-223, miR-100-1/2, miR-125a, miR-143, miR-10a, miR-146, miR-99, miR-100, miR-199a-1, miR-10b, miR-199a-2, miR-221, miR-181a, miR-155, miR-210, miR-213, miR-181b, miR-222, miR-181b-2, miR-21, miR-181b-1, miR-181c, miR-220, miR-181d, miR-223, miR-100-1/2, miR-125a, miR-143, miR-10a, miR-146, miR-99, miR-100, miR-199a-1, miR-10b, miR-199a-2, miR-107, miR-103, miR-103-2, miR-125b-1, miR-205, miR-23a, miR-221, miR-424, miR-301, miR-100, miR-376a, miR-125b-1, miR-21, miR-16-1, miR-181a, miR-181c, miR-92, miR-15, miR-155, let-7f-1, miR-212, miR-107, miR-024-1/2, miR-18a, miR-31, miR-93, miR-224, or let-7d, or any combination thereof.

The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-148a, miR-148b, miR-375, miR-345, miR-142, miR-133a, miR-216, miR-217 or miR-139, or any combination thereof. The one or more mRNAs that may be analyzed can include, but are not limited to, PSCA, Mesothelin, or Osteopontin, or any combination thereof and can be used as specific biomarkers from a vesicle for pancreatic cancer.

A biomarker mutation for pancreatic cancer that can be assessed in a vesicle includes, but is not limited to, a mutation of KRAS, CTNNLB1, AKT, NCOA3, or B-RAF, or any combination of mutations specific for pancreatic cancer. The biomarker can also be BRCA2, PALB2, or p16. Furthermore, a vesicle isolated or assayed can be pancreatic cancer cell specific, or derived from pancreatic cancer cells.

The invention also provides an isolated vesicle comprising one or more pancreatic cancer specific biomarkers, such as listed in FIG. 21. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more pancreatic cancer specific biomarkers, such as listed in FIG. 21. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for pancreatic cancer specific vesicles or vesicles comprising one or more pancreatic cancer specific biomarkers, such as listed in FIG. 21.

One or more pancreatic cancer specific biomarkers, such as listed in FIG. 21, can also be detected by one or more systems disclosed herein, for characterizing a pancreatic cancer. For example, a detection system can comprise one or more probes to detect one or more pancreatic cancer specific biomarkers, such as listed in FIG. 21, of one or more vesicles of a biological sample.

Brain Cancer

Brain cancer (including, but not limited to, gliomas, glioblastomas, meinigiomas, acoustic neuroma/schwannomas, medulloblastoma) specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 22, and can be used to create a brain cancer specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to miR-21, miR-10b, miR-130a, miR-221, miR-125b-1, miR-125b-2, miR-9-2, miR-21, miR-25, or miR-123, or any combination thereof.

The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-128a, miR-181c, miR-181a, or miR-181b, or any combination thereof. The one or more mRNAs that may be analyzed include, but are not limited to, MGMT, which can be used as specific biomarker from a vesicle for brain cancer. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, EGFR.

The invention also provides an isolated vesicle comprising one or more brain cancer specific biomarkers, such as GOPC-ROS1, or those listed in FIG. 22 and in FIG. 1 for brain cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more brain cancer specific biomarkers, such as GOPC-ROS1, or those listed in FIG. 22 and in FIG. 1 for brain cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for brain cancer specific vesicles or vesicles comprising one or more brain cancer specific biomarkers, such as GOPC-ROS1, or those listed in FIG. 22 and in FIG. 1 for brain cancer.

One or more brain cancer specific biomarkers, such as listed in FIG. 22 and in FIG. 1 for brain cancer, can also be detected by one or more systems disclosed herein, for characterizing a brain cancer. For example, a detection system can comprise one or more probes to detect one or more brain cancer specific biomarkers, such as GOPC-ROS1, or those listed in FIG. 22 and in FIG. 1 for brain cancer, of one or more vesicles of a biological sample.

Psoriasis

Psoriasis specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 23, and can be used to create a psoriasis specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-146b, miR-20a, miR-146a, miR-31, miR-200a, miR-17-5p, miR-30e-5p, miR-141, miR-203, miR-142-3p, miR-21, or miR-106a, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such a, but not limited to, miR-125b, miR-99b, miR-122a, miR-197, miR-100, miR-381, miR-518b, miR-524, let-7e, miR-30c, miR-365, miR-133b, miR-10a, miR-133a, miR-22, miR-326, or miR-215, or any combination thereof.

The one or more mRNAs that may be analyzed can include, but are not limited to, IL-20, VEGFR-1, VEGFR-2, VEGFR-3, or EGR1, or any combination thereof and can be used as specific biomarkers from a vesicle for psoriasis. A biomarker mutation for psoriasis that can be assessed in a vesicle includes, but is not limited to, a mutation of MGST2, or any combination of mutations specific for psoriasis.

The invention also provides an isolated vesicle comprising one or more psoriasis specific biomarkers, such as listed in FIG. 23 and in FIG. 1 for psoriasis. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more psoriasis specific biomarkers, such as listed in FIG. 23 and in FIG. 1 for psoriasis. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for psoriasis specific vesicles or vesicles comprising one or more psoriasis specific biomarkers, such as listed in FIG. 23 and in FIG. 1 for psoriasis.

One or more psoriasis specific biomarkers, such as listed in FIG. 23 and in FIG. 1 for psoriasis, can also be detected by one or more systems disclosed herein, for characterizing psoriasis. For example, a detection system can comprise one or more probes to detect one or more psoriasis specific biomarkers, such as listed in FIG. 23 and in FIG. 1 for psoriasis, of one or more vesicles of a biological sample.

Cardiovascular Disease (CVD)

CVD specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 24, and can be used to create a CVD specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-195, miR-208, miR-214, let-7b, let-7c, let-7e, miR-15b, miR-23a, miR-24, miR-27a, miR-27b, miR-93, miR-99b, miR-100, miR-103, miR-125b, miR-140, miR-145, miR-181a, miR-191, miR-195, miR-199a, miR-320, miR-342, miR-451, or miR-499, or any combination thereof.

The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-1, miR-10a, miR-17-5p, miR-19a, miR-19b, miR-20a, miR-20b, miR-26b, miR-28, miR-30e-5p, miR-101, miR-106a, miR-126, miR-222, miR-374, miR-422b, or miR-423, or any combination thereof. The mRNAs that may be analyzed can include, but are not limited to, MRP14, CD69, or any combination thereof and can be used as specific biomarkers from a vesicle for CVD.

A biomarker mutation for CVD that can be assessed in a vesicle includes, but is not limited to, a mutation of MYH7, SCN5A, or CHRM2, or any combination of mutations specific for CVD.

The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, CK-MB, cTnI (cardiac troponin), CRP, BPN, IL-6, MCSF, CD40, CD40L, or any combination thereof. Furthermore, a vesicle isolated or assayed can be a CVD cell specific, or derived from cardiac cells.

The invention also provides an isolated vesicle comprising one or more CVD specific biomarkers, such as listed in FIG. 24 and in FIG. 1 for CVD. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more CVD specific biomarkers, such as listed in FIG. 24 and in FIG. 1 for CVD. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for CVD specific vesicles or vesicles comprising one or more CVD specific biomarkers, such as listed in FIG. 24 and in FIG. 1 for CVD.

One or more CVD specific biomarkers, such as listed in FIG. 24 and in FIG. 1 for CVD, can also be detected by one or more systems disclosed herein, for characterizing a CVD. For example, a detection system can comprise one or more probes to detect one or more CVD specific biomarkers, such as listed in FIG. 24 and in FIG. 1 for CVD, of one or more vesicles of a biological sample.

An increase in an miRNA or combination or miRNA, such as miR-21, miR-129, miR-212, miR-214, miR-134, or a combination thereof (as disclosed in US Publication No. 2010/0010073), can be used to diagnose an increased risk of development or already the existence of cardiac hypertrophy and/or heart failure. A downregulation of miR-182, miR-290, or a combination thereof can be used to diagnose an increased risk of development or already the existence of cardiac hypertrophy and/or heart failure. An increased expression of miR-21, miR-129, miR-212, miR-214, miR-134, or a combination thereof with a reduced expression of miR-182, miR-290, or a combination thereof, may be used to diagnose an increased risk of development or the existence of cardiac hypertrophy and/or heart failure.

Blood Cancers

Hematological malignancies specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 25, and can be used to create a hematological malignancies specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, HOX11, TAL1, LY1, LM01, or LMO2, or any combination thereof and can be used as specific biomarkers from a vesicle for hematological malignancies.

A biomarker mutation for a blood cancer that can be assessed in a vesicle includes, but is not limited to, a mutation of c-kit, PDGFR, or ABL, or any combination of mutations specific for hematological malignancies.

The invention also provides an isolated vesicle comprising one or more blood cancer specific biomarkers, such as listed in FIG. 25 and in FIG. 1 for blood cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more blood cancer specific biomarkers, such as listed in FIG. 25 and in FIG. 1 for blood cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for blood cancer specific vesicles or vesicles comprising one or more blood cancer specific biomarkers, such as listed in FIG. 25 and in FIG. 1 for blood cancer.

One or more blood cancer specific biomarkers, such as listed in FIG. 25 and in FIG. 1 for blood cancer, can also be detected by one or more systems disclosed herein, for characterizing a blood cancer. For example, a detection system can comprise one or more probes to detect one or more blood cancer specific biomarkers, such as listed in FIG. 25 and in FIG. 1 for blood cancer, of one or more vesicles of a biological sample.

The one or more blood cancer specific biomarkers can also be a gene fusion selected from the group consisting of: TTL-ETV6, CDK6-MLL, CDK6-TLX3, ETV6-FLT3, ETV6-RUNX1, ETV6-TTL, MLL-AFF1, MLL-AFF3, MLL-AFF4, MLL-GAS7, TCBA1-ETV6, TCF3-PBX1 or TCF3-TFPT, for acute lymphocytic leukemia (ALL); BCL11B-TLX3, IL2-TNFRFS17, NUP214-ABL1, NUP98-CCDC28A, TAL1-STIL, or ETV6-ABL2, for T-cell acute lymphocytic leukemia (T-ALL); ATIC-ALK, KIAA1618-ALK, MSN-ALK, MYH9-ALK, NPM1-ALK, TGF-ALK or TPM3-ALK, for anaplastic large cell lymphoma (ALCL); BCR-ABL1, BCR-JAK2, ETV6-EVI1, ETV6-MN1 or ETV6-TCBA1, for chronic myelogenous leukemia (CML); CBFB-MYH11, CHIC2-ETV6, ETV6-ABL1, ETV6-ABL2, ETV6-ARNT, ETV6-CDX2, ETV6-HLXB9, ETV6-PER1, MEF2D-DAZAP1, AML-AFF1, MLL-ARHGAP26, MLL-ARHGEF12, MLL-CASC5, MLL-CBL, MLL-CREBBP, MLL-DAB21P, MLL-ELL, MLL-EP300, MLL-EPS15, MLL-FNBP1, MLL-FOXO3A, MLL-GMPS, MLL-GPHN, MLL-MLLT1, MLL-MLLT11, MLL-MLLT3, MLL-MLLT6, MLL-MYO1F, MLL-PICALM, MLL-SEPT2, MLL-SEPT6, MLL-SORBS2, MYST3-SORBS2, MYST-CREBBP, NPM1-MLF1, NUP98-HOXA13, PRDM16-EVI1, RABEP1-PDGFRB, RUNX1-EVI1, RUNX1-MDS1, RUNX1-RPL22, RUNX1-RUNX1T1, RUNX1-SH3D19, RUNX1-USP42, RUNX1-YTHDF2, RUNX1-ZNF687, or TAF15-ZNF-384, for AML; CCND1-FSTL3, for chronic lymphocytic leukemia (CLL); and FLIP1-PDGFRA, FLT3-ETV6, KIAA1509-PDGFRA, PDE4DIP-PDGFRB, NIN-PDGFRB, TP53BP1-PDGFRB, or TPM3-PDGFRB, for hyper eosinophilia/chronic eosinophilia.

The one or more biomarkers for CLL can also include one or more of the following upregulated or overexpressed miRNAs, such as miR-23b, miR-24-1, miR-146, miR-155, miR-195, miR-221, miR-331, miR-29a, miR-195, miR-34a, or miR-29c; one or more of the following downregulated or underexpressed miRs, such as miR-15a, miR-16-1, miR-29 or miR-223, or any combination thereof.

The one or more biomarkers for ALL can also include one or more of the following upregulated or overexpressed miRNAs, such as miR-128b, miR-204, miR-218, miR-331, miR-181b-1, miR-17-92; or any combination thereof.

B-Cell Chronic Lymphocytic Leukemia (B-CLL)

B-CLL specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 26, and can be used to create a B-CLL specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-183-prec, miR-190, miR-24-1-prec, miR-33, miR-19a, miR-140, miR-123, miR-10b, miR-15b-prec, miR-92-1, miR-188, miR-154, miR-217, miR-101, miR-141-prec, miR-153-prec, miR-196-2, miR-134, miR-141, miR-132, miR-192, or miR-181b-prec, or any combination thereof.

The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-213, miR-220, or any combination thereof. The one or more mRNAs that may be analyzed can include, but are not limited to, ZAP70, AdipoR1, or any combination thereof and can be used as specific biomarkers from a vesicle for B-CLL. A biomarker mutation for B-CLL that can be assessed in a vesicle includes, but is not limited to, a mutation of IGHV, P53, ATM, or any combination of mutations specific for B-CLL.

The invention also provides an isolated vesicle comprising one or more B-CLL specific biomarkers, such as BCL3-MYC, MYC-BTG1, BCL7A-MYC, BRWD3-ARHGAP20 or BTG1-MYC, or those listed in FIG. 26. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more B-CLL specific biomarkers, such as BCL3-MYC, MYC-BTG1, BCL7A-MYC, BRWD3-ARHGAP20 or BTG1-MYC, or those listed in FIG. 26. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for B-CLL specific vesicles or vesicles comprising one or more B-CLL specific biomarkers, such as BCL3-MYC, MYC-BTG1, BCL7A-MYC, BRWD3-ARHGAP20 or BTG1-MYC, or those listed in FIG. 26.

One or more B-CLL specific biomarkers, such as BCL3-MYC, MYC-BTG1, BCL7A-MYC, BRWD3-ARHGAP20 or BTG1-MYC, or those listed in FIG. 26, can also be detected by one or more systems disclosed herein, for characterizing a B-CLL. For example, a detection system can comprise one or more probes to detect one or more B-CLL specific biomarkers, such as BCL3-MYC, MYC-BTG1, BCL7A-MYC, BRWD3-ARHGAP20 or BTG1-MYC, or those listed in FIG. 26, of one or more vesicles of a biological sample.

B-Cell Lymphoma

B-cell lymphoma specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 27, and can be used to create a B-cell lymphoma specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-17-92 polycistron, miR-155, miR-210, or miR-21, miR-19a, miR-92, miR-142 miR-155, miR-221 miR-17-92, miR-21, miR-191, miR-205, or any combination thereof. Furthermore the snoRNA that can be used as an exosomal biomarker for B-cell lymphoma can include, but is not limited to, U50.

The invention also provides an isolated vesicle comprising one or more B-cell lymphoma specific biomarkers, such as listed in FIG. 27. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more B-cell lymphoma specific biomarkers, such as listed in FIG. 27. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for B-cell lymphoma specific vesicles or vesicles comprising one or more B-cell lymphoma specific biomarkers, such as listed in FIG. 27.

One or more B-cell lymphoma specific biomarkers, such as listed in FIG. 27, can also be detected by one or more systems disclosed herein, for characterizing a B-cell lymphoma. For example, a detection system can comprise one or more probes to detect one or more B-cell lymphoma specific biomarkers, such as listed in FIG. 27, of one or more vesicles of a biological sample.

Diffuse Large B-Cell Lymphoma (DLBCL)

DLBCL specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 28, and can be used to create a DLBCL specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-17-92, miR-155, miR-210, or miR-21, or any combination thereof. The one or more mRNAs that may be analyzed can include, but are not limited to, A-myb, LMO2, JNK3, CD10, bcl-6, Cyclin D2, IRF4, Flip, or CD44, or any combination thereof and can be used as specific biomarkers from a vesicle for DLBCL.

The invention also provides an isolated vesicle comprising one or more DLBCL specific biomarkers, such as CITTA-BCL6, CLTC-ALK, IL21R-BCL6, PIM1-BCL6, TFCR-BCL6, IKZF1-BCL6 or SEC31A-ALK, or those listed in FIG. 28. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more DLBCL specific biomarkers, such as CITTA-BCL6, CLTC-ALK, IL21R-BCL6, PIM1-BCL6, TFCR-BCL6, IKZF1-BCL6 or SEC31A-ALK, or those listed in FIG. 28. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for DLBCL specific vesicles or vesicles comprising one or more DLBCL specific biomarkers, such as CITTA-BCL6, CLTC-ALK, IL21R-BCL6, PIM1-BCL6, TFCR-BCL6, IKZF1-BCL6 or SEC31A-ALK, or those listed in FIG. 28.

One or more DLBCL specific biomarkers, such as CITTA-BCL6, CLTC-ALK, IL21R-BCL6, PIM1-BCL6, TFCR-BCL6, IKZF1-BCL6 or SEC31A-ALK, or those listed in FIG. 28, can also be detected by one or more systems disclosed herein, for characterizing a DLBCL. For example, a detection system can comprise one or more probes to detect one or more DLBCL specific biomarkers, such as CITTA-BCL6, CLTC-ALK, IL21R-BCL6, PIM1-BCL6, TFCR-BCL6, IKZF1-BCL6 or SEC31A-ALK, or those listed in FIG. 28, of one or more vesicles of a biological sample.

Burkitt's Lymphoma

Burkitt's lymphoma specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 29, and can be used to create a Burkitt's lymphoma specific biosignature. For example, the biosignature can also comprise one or more underexpressed miRs such as, but not limited to, pri-miR-155, or any combination thereof. The one or more mRNAs that may be analyzed can include, but are not limited to, MYC, TERT, NS, NP, MAZ, RCF3, BYSL, IDE3, CDC7, TCL1A, AUTS2, MYBL1, BMP7, ITPR3, CDC2, BACK2, TTK, MME, ALOX5, or TOP1, or any combination thereof and can be used as specific biomarkers from a vesicle for Burkitt's lymphoma. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, BCL6, KI-67, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more Burkitt's lymphoma specific biomarkers, such as IGH-MYC, LCP1-BCL6, or those listed in FIG. 29. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more Burkitt's lymphoma specific biomarkers, such as IGH-MYC, LCP1-BCL6, or those listed in FIG. 29. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for Burkitt's lymphoma specific vesicles or vesicles comprising one or more Burkitt's lymphoma specific biomarkers, such as IGH-MYC, LCP1-BCL6, or those listed in FIG. 29.

One or more Burkitt's lymphoma specific biomarkers, such as IGH-MYC, LCP1-BCL6, or those listed in FIG. 29, can also be detected by one or more systems disclosed herein, for characterizing a Burkitt's lymphoma. For example, a detection system can comprise one or more probes to detect one or more Burkitt's lymphoma specific biomarkers, such as IGH-MYC, LCP1-BCL6, or those listed in FIG. 29, of one or more vesicles of a biological sample.

Hepatocellular Carcinoma

Hepatocellular carcinoma specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 30 and can be used to create a hepatocellular carcinoma specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-221. The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, let-7a-1, let-7a-2, let-7a-3, let-7b, let-7c, let-7d, let-7e, let-7f-2, let-fg, miR-122a, miR-124a-2, miR-130a, miR-132, miR-136, miR-141, miR-142, miR-143, miR-145, miR-146, miR-150, miR-155(BIC), miR-181a-1, miR-181a-2, miR-181c, miR-195, miR-199a-1-5p, miR-199a-2-5p, miR-199b, miR-200b, miR-214, miR-223, or pre-miR-594, or any combination thereof. The one or more mRNAs that may be analyzed can include, but are not limited to, FAT10.

The one or more biomarkers of a biosignature can also be used to characterize hepatitis C virus-associated hepatocellular carcinoma. The one or more biomarkers can be a miRNA, such as an overexpressed or underexpressed miRNA. For example, the upregulated or overexpressed miRNA can be miR-122, miR-100, or miR-10a and the downregulated miRNA can be miR-198 or miR-145.

The invention also provides an isolated vesicle comprising one or more hepatocellular carcinoma specific biomarkers, such as listed in FIG. 30 and in FIG. 1 for hepatocellular carcinoma. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more hepatocellular carcinoma specific biomarkers, such as listed in FIG. 30 and in FIG. 1 for hepatocellular carcinoma. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for hepatocellular carcinoma specific vesicles or vesicles comprising one or more hepatocellular carcinoma specific biomarkers, such as listed in FIG. 30 and in FIG. 1 for hepatocellular carcinoma.

One or more hepatocellular carcinoma specific biomarkers, such as listed in FIG. 30 and in FIG. 1 for hepatocellular carcinoma, can also be detected by one or more systems disclosed herein, for characterizing a hepatocellular carcinoma. For example, a detection system can comprise one or more probes to detect one or more hepatocellular carcinoma specific biomarkers, such as listed in FIG. 30 and in FIG. 1 for hepatocellular carcinoma, of one or more vesicles of a biological sample.

Cervical Cancer

Cervical cancer specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 31, and can be used to create a cervical cancer specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, HPV E6, HPV E7, or p53, or any combination thereof and can be used as specific biomarkers from a vesicle for cervical cancer.

The invention also provides an isolated vesicle comprising one or more cervical cancer specific biomarkers, such as listed in FIG. 31 and in FIG. 1 for cervical cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more cervical cancer specific biomarkers, such as listed in FIG. 31 and in FIG. 1 for cervical cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for cervical cancer specific vesicles or vesicles comprising one or more cervical cancer specific biomarkers, such as listed in FIG. 31 and in FIG. 1 for cervical cancer.

One or more cervical cancer specific biomarkers, such as listed in FIG. 31 and in FIG. 1 for cervical cancer, can also be detected by one or more systems disclosed herein, for characterizing a cervical cancer. For example, a detection system can comprise one or more probes to detect one or more cervical cancer specific biomarkers, such as listed in FIG. 31 and in FIG. 1 for cervical cancer, of one or more vesicles of a biological sample.

Endometrial Cancer

Endometrial cancer specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 32 and can be used to create a endometrial cancer specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-185, miR-106a, miR-181a, miR-210, miR-423, miR-103, miR-107, or let-7c, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-71, miR-221, miR-193, miR-152, or miR-30c, or any combination thereof.

A biomarker mutation for endometrial cancer that can be assessed in a vesicle includes, but is not limited to, a mutation of PTEN, K-RAS, B-catenin, p53, Her2/neu, or any combination of mutations specific for endometrial cancer. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, NLRP7, AlphaV Beta6 integrin, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more endometrial cancer specific biomarkers, such as listed in FIG. 32 and in FIG. 1 for endometrial cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more endometrial cancer specific biomarkers, such as listed in FIG. 32 and in FIG. 1 for endometrial cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for endometrial cancer specific vesicles or vesicles comprising one or more endometrial cancer specific biomarkers, such as listed in FIG. 32 and in FIG. 1 for endometrial cancer.

One or more endometrial cancer specific biomarkers, such as listed in FIG. 32 and in FIG. 1 for endometrial cancer, can also be detected by one or more systems disclosed herein, for characterizing a endometrial cancer. For example, a detection system can comprise one or more probes to detect one or more endometrial cancer specific biomarkers, such as listed in FIG. 32 and in FIG. 1 for endometrial cancer, of one or more vesicles of a biological sample.

Head and Neck Cancer

Head and neck cancer specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 33, and can be used to create a head and neck cancer specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-21, let-7, miR-18, miR-29c, miR-142-3p, miR-155, miR-146b, miR-205, or miR-21, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-494. The one or more mRNAs that may be analyzed include, but are not limited to, HPV E6, HPV E7, p53, IL-8, SAT, H3FA3, or EGFR, or any combination thereof and can be used as specific biomarkers from a vesicle for head and neck cancer.

A biomarker mutation for head and neck cancer that can be assessed in a vesicle includes, but is not limited to, a mutation of GSTM1, GSTT1, GSTP1, OGG1, XRCC1, XPD, RAD51, EGFR, p53, or any combination of mutations specific for head and neck cancer. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, EGFR, EphB4, or EphB2, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more head and neck cancer specific biomarkers, such as CHCHD7-PLAG1, CTNNB1-PLAG1, FHIT-HMGA2, HMGA2-NFIB, LIFR-PLAG1, or TCEA1-PLAG1, or those listed in FIG. 33 and in FIG. 1 for head and neck cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more head and neck cancer specific biomarkers, such as CHCHD7-PLAG1, CTNNB1-PLAG1, FHIT-HMGA2, HMGA2-NFIB, LIFR-PLAG1, or TCEA1-PLAG1, or those listed in FIG. 33 and in FIG. 1 for head and neck cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for head and neck cancer specific vesicles or vesicles comprising one or more head and neck cancer specific biomarkers, such as CHCHD7-PLAG1, CTNNB1-PLAG1, FHIT-HMGA2, HMGA2-NFIB, LIFR-PLAG1, or TCEA1-PLAG1, or those listed in FIG. 33 and in FIG. 1 for head and neck cancer.

One or more head and neck cancer specific biomarkers, such as listed in FIG. 33 and in FIG. 1 for head and neck cancer, can also be detected by one or more systems disclosed herein, for characterizing a head and neck cancer. For example, a detection system can comprise one or more probes to detect one or more head and neck cancer specific biomarkers, such as CHCHD7-PLAG1, CTNNB1-PLAG1, FHIT-HMGA2, HMGA2-NFIB, LIFR-PLAG1, or TCEA1-PLAG1, or those listed in FIG. 33 and in FIG. 1 for head and neck cancer, of one or more vesicles of a biological sample.

Inflammatory Bowel Disease (IBD)

IBD specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 34, and can be used to create a IBD specific biosignature. The one or more mRNAs that may be analyzed can include, but are not limited to, Trypsinogen IV, SERT, or any combination thereof and can be used as specific biomarkers from a vesicle for IBD.

A biomarker mutation for IBD that can be assessed in a vesicle can include, but is not limited to, a mutation of CARD15 or any combination of mutations specific for IBD. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, II-16, II-1beta, II-12, TNF-alpha, interferon gamma, II-6, Rantes, MCP-1, Resistin, or 5-HT, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more IBD specific biomarkers, such as listed in FIG. 34 and in FIG. 1 for IBD. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more IBD specific biomarkers, such as listed in FIG. 34 and in FIG. 1 for IBD. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for IBD specific vesicles or vesicles comprising one or more IBD specific biomarkers, such as listed in FIG. 34 and in FIG. 1 for IBD.

One or more IBD specific biomarkers, such as listed in FIG. 34 and in FIG. 1 for IBD, can also be detected by one or more systems disclosed herein, for characterizing a IBD. For example, a detection system can comprise one or more probes to detect one or more IBD specific biomarkers, such as listed in FIG. 34 and in FIG. 1 for IBD, of one or more vesicles of a biological sample.

Diabetes

Diabetes specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 35, and can be used to create a diabetes specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, Il-8, CTSS, ITGB2, HLA-DRA, CD53, PLAG27, or MMP9, or any combination thereof and can be used as specific biomarkers from a vesicle for diabetes. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, RBP4.

The invention also provides an isolated vesicle comprising one or more diabetes specific biomarkers, such as listed in FIG. 35 and in FIG. 1 for diabetes. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more diabetes specific biomarkers, such as listed in FIG. 35 and in FIG. 1 for diabetes. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for diabetes specific vesicles or vesicles comprising one or more diabetes specific biomarkers, such as listed in FIG. 35 and in FIG. 1 for diabetes.

One or more diabetes specific biomarkers, such as listed in FIG. 35 and in FIG. 1 for diabetes, can also be detected by one or more systems disclosed herein, for characterizing diabetes. For example, a detection system can comprise one or more probes to detect one or more diabetes specific biomarkers, such as listed in FIG. 35 and in FIG. 1 for diabetes, of one or more vesicles of a biological sample.

Barrett's Esophagus

Barrett's Esophagus specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 36, and can be used to create a Barrett's Esophagus specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-21, miR-143, miR-145, miR-194, or miR-215, or any combination thereof. The one or more mRNAs that may be analyzed include, but are not limited to, S100A2, S100A4, or any combination thereof and can be used as specific biomarkers from a vesicle for Barrett's Esophagus.

A biomarker mutation for Barrett's Esophagus that can be assessed in a vesicle includes, but is not limited to, a mutation of p53 or any combination of mutations specific for Barrett's Esophagus. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, p53, MUC1, MUC2, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more Barrett's Esophagus specific biomarkers, such as listed in FIG. 36 and in FIG. 1 for Barrett's Esophagus. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more Barrett's Esophagus specific biomarkers, such as listed in FIG. 36 and in FIG. 1 for Barrett's Esophagus. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for Barrett's Esophagus specific vesicles or vesicles comprising one or more Barrett's Esophagus specific biomarkers, such as listed in FIG. 36 and in FIG. 1 for Barrett's Esophagus.

One or more Barrett's Esophagus specific biomarkers, such as listed in FIG. 36 and in FIG. 1 for Barrett's Esophagus, can also be detected by one or more systems disclosed herein, for characterizing a Barrett's Esophagus. For example, a detection system can comprise one or more probes to detect one or more Barrett's Esophagus specific biomarkers, such as listed in FIG. 36 and in FIG. 1 for Barrett's Esophagus, of one or more vesicles of a biological sample.

Fibromyalgia

Fibromyalgia specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 37, and can be used to create a fibromyalgia specific biosignature. The one or more mRNAs that may be analyzed can include, but are not limited to, NR2D which can be used as a specific biomarker from a vesicle for fibromyalgia.

The invention also provides an isolated vesicle comprising one or more fibromyalgia specific biomarkers, such as listed in FIG. 37 and in FIG. 1 for fibromyalgia. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more fibromyalgia specific biomarkers, such as listed in FIG. 37 and in FIG. 1 for fibromyalgia. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for fibromyalgia specific vesicles or vesicles comprising one or more fibromyalgia specific biomarkers, such as listed in FIG. 37 and in FIG. 1 for fibromyalgia.

One or more fibromyalgia specific biomarkers, such as listed in FIG. 37 and in FIG. 1 for fibromyalgia, can also be detected by one or more systems disclosed herein, for characterizing a fibromyalgia. For example, a detection system can comprise one or more probes to detect one or more fibromyalgia specific biomarkers, such as listed in FIG. 37 and in FIG. 1 for fibromyalgia, of one or more vesicles of a biological sample.

Stroke

Stroke specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 38, and can be used to create a stroke specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, MMP9, S100-P, S100A12, S100A9, coag factor V, ArginaseI, CA-IV, monocarboxylic acid transporter, ets-2, EIF2alpha, cytoskeleton associated protein 4, N-formylpeptide receptor, Ribonuclease2, N-acetylneuraminate pyruvate lyase, BCL-6, or Glycogen phosphorylase, or any combination thereof and can be used as specific biomarkers from a vesicle for stroke.

The invention also provides an isolated vesicle comprising one or more stroke specific biomarkers, such as listed in FIG. 38 and in FIG. 1 for stroke. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more stroke specific biomarkers, such as listed in FIG. 38 and in FIG. 1 for stroke. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for stroke specific vesicles or vesicles comprising one or more stroke specific biomarkers, such as listed in FIG. 38 and in FIG. 1 for stroke.

One or more stroke specific biomarkers, such as listed in FIG. 38 and in FIG. 1 for stroke, can also be detected by one or more systems disclosed herein, for characterizing a stroke. For example, a detection system can comprise one or more probes to detect one or more stroke specific biomarkers, such as listed in FIG. 38 and in FIG. 1 for stroke, of one or more vesicles of a biological sample.

Multiple Sclerosis (MS)

MS specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 39, and can be used to create a MS specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, IL-6, IL-17, PAR-3, IL-17, T1/ST2, JunD, 5-LO, LTA4H, MBP, PLP, or alpha-beta crystallin, or any combination thereof and can be used as specific biomarkers from a vesicle for MS.

The invention also provides an isolated vesicle comprising one or more MS specific biomarkers, such as listed in FIG. 39 and in FIG. 1 for MS. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more MS specific biomarkers, such as listed in FIG. 39 and in FIG. 1 for MS. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for MS specific vesicles or vesicles comprising one or more MS specific biomarkers, such as listed in FIG. 39 and in FIG. 1 for MS.

One or more MS specific biomarkers, such as listed in FIG. 39 and in FIG. 1 for MS, can also be detected by one or more systems disclosed herein, for characterizing a MS. For example, a detection system can comprise one or more probes to detect one or more MS specific biomarkers, such as listed in FIG. 39 and in FIG. 1 for MS, of one or more vesicles of a biological sample.

Parkinson's Disease

Parkinson's disease specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 40, and can be used to create a Parkinson's disease specific biosignature. For example, the biosignature can include, but is not limited to, one or more underexpressed miRs such as miR-133b. The one or more mRNAs that may be analyzed can include, but are not limited to Nurr1, BDNF, TrkB, gstm1, or 5100 beta, or any combination thereof and can be used as specific biomarkers from a vesicle for Parkinson's disease.

A biomarker mutation for Parkinson's disease that can be assessed in a vesicle includes, but is not limited to, a mutation of FGF20, alpha-synuclein, FGF20, NDUFV2, FGF2, CALB1, B2M, or any combination of mutations specific for Parkinson's disease. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, apo-H, Ceruloplasmin, BDNF, IL-8, Beta2-microglobulin, apoAII, tau, ABeta1-42, DJ-1, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more Parkinson's disease specific biomarkers, such as listed in FIG. 40 and in FIG. 1 for Parkinson's disease A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more Parkinson's disease specific biomarkers, such as listed in FIG. 40 and in FIG. 1 for Parkinson's disease. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for Parkinson's disease specific vesicles or vesicles comprising one or more Parkinson's disease specific biomarkers, such as listed in FIG. 40 and in FIG. 1 for Parkinson's disease.

One or more Parkinson's disease specific biomarkers, such as listed in FIG. 40 and in FIG. 1 for Parkinson's disease, can also be detected by one or more systems disclosed herein, for characterizing a Parkinson's disease. For example, a detection system can comprise one or more probes to detect one or more Parkinson's disease specific biomarkers, such as listed in FIG. 40 and in FIG. 1 for Parkinson's disease, of one or more vesicles of a biological sample.

Rheumatic Disease

Rheumatic disease specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 41, and can be used to create a rheumatic disease specific biosignature. For example, the biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-146a, miR-155, miR-132, miR-16, or miR-181, or any combination thereof. The one or more mRNAs that may be analyzed can include, but are not limited to, HOXD10, HOXD11, HOXD13, CCL8, LIM homeobox2, or CENP-E, or any combination thereof and can be used as specific biomarkers from a vesicle for rheumatic disease. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, TNFα.

The invention also provides an isolated vesicle comprising one or more rheumatic disease specific biomarkers, such as listed in FIG. 41 and in FIG. 1 for rheumatic disease. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more rheumatic disease specific biomarkers, such as listed in FIG. 41 and in FIG. 1 for rheumatic disease. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for rheumatic disease specific vesicles or vesicles comprising one or more rheumatic disease specific biomarkers, such as listed in FIG. 41 and in FIG. 1 for rheumatic disease.

One or more rheumatic disease specific biomarkers, such as listed in FIG. 41 and in FIG. 1 for rheumatic disease, can also be detected by one or more systems disclosed herein, for characterizing a rheumatic disease. For example, a detection system can comprise one or more probes to detect one or more rheumatic disease specific biomarkers, such as listed in FIG. 41 and in FIG. 1 for rheumatic disease, of one or more vesicles of a biological sample.

Alzheimer's Disease

Alzheimer's disease specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 42, and can be used to create a Alzheimers disease specific biosignature. For example, the biosignature can also comprise one or more underexpressed miRs such as miR-107, miR-29a, miR-29b-1, or miR-9, or any combination thereof. The biosignature can also comprise one or more overexpressed miRs such as miR-128 or any combination thereof.

The one or more mRNAs that may be analyzed can include, but are not limited to, HIF-1α, BACE1, Reelin, CHRNA7, or 3Rtau/4Rtau, or any combination thereof and can be used as specific biomarkers from a vesicle for Alzheimer's disease.

A biomarker mutation for Alzheimer's disease that can be assessed in a vesicle includes, but is not limited to, a mutation of APP, presenilin1, presenilin2, APOE4, or any combination of mutations specific for Alzheimer's disease. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, BACE1, Reelin, Cystatin C, Truncated Cystatin C, Amyloid Beta, C3a, t-Tau, Complement factor H, or alpha-2-macroglobulin, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more Alzheimer's disease specific biomarkers, such as listed in FIG. 42 and in FIG. 1 for Alzheimer's disease. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more Alzheimer's disease specific biomarkers, such as listed in FIG. 42 and in FIG. 1 for Alzheimer's disease. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for Alzheimer's disease specific vesicles or vesicles comprising one or more Alzheimer's disease specific biomarkers, such as listed in FIG. 42 and in FIG. 1 for Alzheimer's disease.

One or more Alzheimer's disease specific biomarkers, such as listed in FIG. 42 and in FIG. 1 for Alzheimer's disease, can also be detected by one or more systems disclosed herein, for characterizing a Alzheimer's disease. For example, a detection system can comprise one or more probes to detect one or more Alzheimer's disease specific biomarkers, such as listed in FIG. 42 and in FIG. 1 for Alzheimer's disease, of one or more vesicles of a biological sample.

Prion Disease

Prion specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 43, and can be used to create a prion specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, Amyloid B4, App, IL-1R1, or SOD1, or any combination thereof and can be used as specific biomarkers from a vesicle for a prion. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, PrP(c), 14-3-3, NSE, S-100, Tau, AQP-4, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more prion disease specific biomarkers, such as listed in FIG. 43 and in FIG. 1 for prion disease. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more prion disease specific biomarkers, such as listed in FIG. 43 and in FIG. 1 for prion disease. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for prion disease specific vesicles or vesicles comprising one or more prion disease specific biomarkers, such as listed in FIG. 43 and in FIG. 1 for prion disease.

One or more prion disease specific biomarkers, such as listed in FIG. 43 and in FIG. 1 for prion disease, can also be detected by one or more systems disclosed herein, for characterizing a prion disease. For example, a detection system can comprise one or more probes to detect one or more prion disease specific biomarkers, such as listed in FIG. 43 and in FIG. 1 for prion disease, of one or more vesicles of a biological sample.

Sepsis

Sepsis specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 44, and can be used to create a sepsis specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, 15-Hydroxy-PG dehydrogenase (up), LAIR1 (up), NFKB1A (up), TLR2, PGLYPR1, TLR4, MD2, TLR5, IFNAR2, IRAK2, IRAK3, IRAK4, PI3K, PI3KCB, MAP2K6, MAPK14, NFKB1A, NFKB1, IL1R1, MAP2K1IP1, MKNK1, FAS, CASP4, GADD45B, SOCS3, TNFSF10, TNFSF13B, OSM, HGF, or IL18R1, or any combination thereof and can be used as specific biomarkers from a vesicle for sepsis.

The invention also provides an isolated vesicle comprising one or more sepsis specific biomarkers, such as listed in FIG. 44. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more sepsis specific biomarkers, such as listed in FIG. 44. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for sepsis specific vesicles or vesicles comprising one or more sepsis specific biomarkers, such as listed in FIG. 44.

One or more sepsis specific biomarkers, such as listed in FIG. 44, can also be detected by one or more systems disclosed herein, for characterizing a sepsis. For example, a detection system can comprise one or more probes to detect one or more sepsis specific biomarkers, such as listed in FIG. 44, of one or more vesicles of a biological sample.

Chronic Neuropathic Pain

Chronic neuropathic pain (CNP) specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 45, and can be used to create a CNP specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, ICAM-1 (rodent), CGRP (rodent), TIMP-1 (rodent), CLR-1 (rodent), HSP-27 (rodent), FABP (rodent), or apolipoprotein D (rodent), or any combination thereof and can be used as specific biomarkers from a vesicle for CNP. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, chemokines, chemokine receptors (CCR2/4), or any combination thereof.

The invention also provides an isolated vesicle comprising one or more chronic neuropathic pain specific biomarkers, such as listed in FIG. 45 and in FIG. 1 for chronic neuropathic pain. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more chronic neuropathic pain specific biomarkers, such as listed in FIG. 45 and in FIG. 1 for chronic neuropathic pain. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for chronic neuropathic pain specific vesicles or vesicles comprising one or more chronic neuropathic pain specific biomarkers, such as listed in FIG. 45 and in FIG. 1 for chronic neuropathic pain.

One or more chronic neuropathic pain specific biomarkers, such as listed in FIG. 45 and in FIG. 1 for chronic neuropathic pain, can also be detected by one or more systems disclosed herein, for characterizing a chronic neuropathic pain. For example, a detection system can comprise one or more probes to detect one or more chronic neuropathic pain specific biomarkers, such as listed in FIG. 45 and in FIG. 1 for chronic neuropathic pain, of one or more vesicles of a biological sample.

Peripheral Neuropathic Pain

Peripheral neuropathic pain (PNP) specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 46, and can be used to create a PNP specific biosignature. For example, the protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, OX42, ED9, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more peripheral neuropathic pain specific biomarkers, such as listed in FIG. 46 and in FIG. 1 for peripheral neuropathic pain. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more peripheral neuropathic pain specific biomarkers, such as listed in FIG. 46 and in FIG. 1 for peripheral neuropathic pain. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for peripheral neuropathic pain specific vesicles or vesicles comprising one or more peripheral neuropathic pain specific biomarkers, such as listed in FIG. 46 and in FIG. 1 for peripheral neuropathic pain.

One or more peripheral neuropathic pain specific biomarkers, such as listed in FIG. 46 and in FIG. 1 for peripheral neuropathic pain, can also be detected by one or more systems disclosed herein, for characterizing a peripheral neuropathic pain. For example, a detection system can comprise one or more probes to detect one or more peripheral neuropathic pain specific biomarkers, such as listed in FIG. 46 and in FIG. 1 for peripheral neuropathic pain, of one or more vesicles of a biological sample.

Schizophrenia

Schizophrenia specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 47, and can be used to create a schizophrenia specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-181b. The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-7, miR-24, miR-26b, miR-29b, miR-30b, miR-30e, miR-92, or miR-195, or any combination thereof.

The one or more mRNAs that may be analyzed can include, but are not limited to, IFITM3, SERPINA3, GLS, or ALDH7A1BASP1, or any combination thereof and can be used as specific biomarkers from a vesicle for schizophrenia. A biomarker mutation for schizophrenia that can be assessed in a vesicle includes, but is not limited to, a mutation of to DISC1, dysbindin, neuregulin-1, seratonin 2a receptor, NURR1, or any combination of mutations specific for schizophrenia.

The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, ATP5B, ATP5H, ATP6V1B, DNM1, NDUFV2, NSF, PDHB, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more schizophrenia specific biomarkers, such as listed in FIG. 47 and in FIG. 1 for schizophrenia. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more schizophrenia specific biomarkers, such as listed in FIG. 47 and in FIG. 1 for schizophrenia. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for schizophrenia specific vesicles or vesicles comprising one or more schizophrenia specific biomarkers, such as listed in FIG. 47 and in FIG. 1 for schizophrenia.

One or more schizophrenia specific biomarkers, such as listed in FIG. 47 and in FIG. 1 for schizophrenia, can also be detected by one or more systems disclosed herein, for characterizing a schizophrenia. For example, a detection system can comprise one or more probes to detect one or more schizophrenia specific biomarkers, such as listed in FIG. 47 and in FIG. 1 for schizophrenia, of one or more vesicles of a biological sample.

Bipolar Disease

Bipolar disease specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 48, and can be used to create a bipolar disease specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, FGF2, ALDH7A1, AGXT2L1, AQP4, or PCNT2, or any combination thereof and can be used as specific biomarkers from a vesicle for bipolar disease. A biomarker mutation for bipolar disease that can be assessed in a vesicle includes, but is not limited to, a mutation of Dysbindin, DAOA/G30, DISC1, neuregulin-1, or any combination of mutations specific for bipolar disease.

The invention also provides an isolated vesicle comprising one or more bipolar disease specific biomarkers, such as listed in FIG. 48. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more bipolar disease specific biomarkers, such as listed in FIG. 48. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for bipolar disease specific vesicles or vesicles comprising one or more bipolar disease specific biomarkers, such as listed in FIG. 48.

One or more bipolar disease specific biomarkers, such as listed in FIG. 48, can also be detected by one or more systems disclosed herein, for characterizing a bipolar disease. For example, a detection system can comprise one or more probes to detect one or more bipolar disease specific biomarkers, such as listed in FIG. 48, of one or more vesicles of a biological sample.

Depression

Depression specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 49, and can be used to create a depression specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, FGFR1, FGFR2, FGFR3, or AQP4, or any combination thereof can also be used as specific biomarkers from a vesicle for depression.

The invention also provides an isolated vesicle comprising one or more depression specific biomarkers, such as listed in FIG. 49. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more depression specific biomarkers, such as listed in FIG. 49. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for depression specific vesicles or vesicles comprising one or more depression specific biomarkers, such as listed in FIG. 49.

One or more depression specific biomarkers, such as listed in FIG. 49, can also be detected by one or more systems disclosed herein, for characterizing a depression. For example, a detection system can comprise one or more probes to detect one or more depression specific biomarkers, such as listed in FIG. 49, of one or more vesicles of a biological sample.

Gastrointestinal Stromal Tumor (GIST)

GIST specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 50, and can be used to create a GIST specific biosignature. For example, the one or more mRNAs that may be analyzed can include, but are not limited to, DOG-1, PKC-theta, KIT, GPR20, PRKCQ, KCNK3, KCNH2, SCG2, TNFRSF6B, or CD34, or any combination thereof and can be used as specific biomarkers from a vesicle for GIST.

A biomarker mutation for GIST that can be assessed in a vesicle includes, but is not limited to, a mutation of PKC-theta or any combination of mutations specific for GIST. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, PDGFRA, c-kit, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more GIST specific biomarkers, such as listed in FIG. 50 and in FIG. 1 for GIST. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more GIST specific biomarkers, such as listed in FIG. 50 and in FIG. 1 for GIST. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for GIST specific vesicles or vesicles comprising one or more GIST specific biomarkers, such as listed in FIG. 50 and in FIG. 1 for GIST.

One or more GIST specific biomarkers, such as listed in FIG. 50 and in FIG. 1 for GIST, can also be detected by one or more systems disclosed herein, for characterizing a GIST. For example, a detection system can comprise one or more probes to detect one or more GIST specific biomarkers, such as listed in FIG. 50 and in FIG. 1 for GIST, of one or more vesicles of a biological sample.

Renal Cell Carcinoma

Renal cell carcinoma specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 51, and can be used to create a renal cell carcinoma specific biosignature. For example, the biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-141, miR-200c, or any combination thereof. The one or more upregulated or overexpressed miRNA can be miR-28, miR-185, miR-27, miR-let-7f-2, or any combination thereof.

The one or more mRNAs that may be analyzed can include, but are not limited to, laminin receptor 1, betaig-h3, Galectin-1, a-2 Macroglobulin, Adipophilin, Angiopoietin 2, Caldesmon 1, Class II MHC-associated invariant chain (CD74), Collagen IV-a1, Complement component, Complement component 3, Cytochrome P450, subfamily IIJ polypeptide 2, Delta sleep-inducing peptide, Fc g receptor IIIa (CD16), HLA-B, HLA-DRa, HLA-DRb, HLA-SB, IFN-induced transmembrane protein 3, IFN-induced transmembrane protein 1, or Lysyl Oxidase, or any combination thereof and can be used as specific biomarkers from a vesicle for renal cell carcinoma.

A biomarker mutation for renal cell carcinoma that can be assessed in a vesicle includes, but is not limited to, a mutation of VHL or any combination of mutations specific renal cell carcinoma.

The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, IF1alpha, VEGF, PDGFRA, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more RCC specific biomarkers, such as ALPHA-TFEB, NONO-TFE3, PRCC-TFE3, SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFEB, or those listed in FIG. 51 and in FIG. 1 for RCC. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more RCC specific biomarkers, such as ALPHA-TFEB, NONO-TFE3, PRCC-TFE3, SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFE, or those listed in FIG. 51 and in FIG. 1 for RCC. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for RCC specific vesicles or vesicles comprising one or more RCC specific biomarkers, such as ALPHA-TFEB, NONO-TFE3, PRCC-TFE3, SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFE, or those listed in FIG. 51 and in FIG. 1 for RCC.

One or more RCC specific biomarkers, such as ALPHA-TFEB, NONO-TFE3, PRCC-TFE3, SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFE, or those listed in FIG. 51 and in FIG. 1 for RCC, can also be detected by one or more systems disclosed herein, for characterizing a RCC. For example, a detection system can comprise one or more probes to detect one or more RCC specific biomarkers, such as ALPHA-TFEB, NONO-TFE3, PRCC-TFE3, SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFE, or those listed in FIG. 51 and in FIG. 1 for RCC, of one or more vesicles of a biological sample.

Cirrhosis

Cirrhosis specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 52, and can be used to create a cirrhosis specific biosignature. The one or more mRNAs that may be analyzed include, but are not limited to, NLT, which can be used as aspecific biomarker from a vesicle for cirrhosis.

The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, NLT, HBsAG, AST, YKL-40, Hyaluronic acid, TIMP-1, alpha 2 macroglobulin, a-1-antitrypsin P1Z allele, haptoglobin, or acid phosphatase ACP AC, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more cirrhosis specific biomarkers, such as those listed in FIG. 52 and in FIG. 1 for cirrhosis. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more cirrhosis specific biomarkers, such as those listed in FIG. 52 and in FIG. 1 for cirrhosis. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for cirrhosis specific vesicles or vesicles comprising one or more cirrhosis specific biomarkers, such as those listed in FIG. 52 and in FIG. 1 for cirrhosis.

One or more cirrhosis specific biomarkers, such as those listed in FIG. 52 and in FIG. 1 for cirrhosis, can also be detected by one or more systems disclosed herein, for characterizing cirrhosis. For example, a detection system can comprise one or more probes to detect one or more cirrhosis specific biomarkers, such as those listed in FIG. 52 and in FIG. 1 for cirrhosis, of one or more vesicles of a biological sample.

Esophageal Cancer

Esophageal cancer specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 53, and can be used to create a esophageal cancer specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-192, miR-194, miR-21, miR-200c, miR-93, miR-342, miR-152, miR-93, miR-25, miR-424, or miR-151, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-27b, miR-205, miR-203, miR-342, let-7c, miR-125b, miR-100, miR-152, miR-192, miR-194, miR-27b, miR-205, miR-203, miR-200c, miR-99a, miR-29c, miR-140, miR-103, or miR-107, or any combination thereof. The one or more mRNAs that may be analyzed include, but are not limited to, MTHFR and can be used as specific biomarkers from a vesicle for esophageal cancer.

The invention also provides an isolated vesicle comprising one or more esophageal cancer specific biomarkers, such as listed in FIG. 53 and in FIG. 1 for esophageal cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more esophageal cancer specific biomarkers, such as listed in FIG. 53 and in FIG. 1 for esophageal cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for esophageal cancer specific vesicles or vesicles comprising one or more esophageal cancer specific biomarkers, such as listed in FIG. 53 and in FIG. 1 for esophageal cancer.

One or more esophageal cancer specific biomarkers, such as listed in FIG. 53 and in FIG. 1 for esophageal cancer, can also be detected by one or more systems disclosed herein, for characterizing a esophageal cancer. For example, a detection system can comprise one or more probes to detect one or more esophageal cancer specific biomarkers, such as listed in FIG. 53 and in FIG. 1 for esophageal cancer, of one or more vesicles of a biological sample.

Gastric Cancer

Gastric cancer specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 54, and can be used to create a gastric cancer specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-106a, miR-21, miR-191, miR-223, miR-24-1, miR-24-2, miR-107, miR-92-2, miR-214, miR-25, or miR-221, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, let-7a.

The one or more mRNAs that may be analyzed include, but are not limited to, RRM2, EphA4, or survivin, or any combination thereof and can be used as specific biomarkers from a vesicle for gastric cancer. A biomarker mutation for gastric cancer that can be assessed in a vesicle includes, but is not limited to, a mutation of APC or any combination of mutations specific for gastric cancer. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to EphA4.

The invention also provides an isolated vesicle comprising one or more gastric cancer specific biomarkers, such as listed in FIG. 54. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more gastric cancer specific biomarkers, such as listed in FIG. 54. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for gastric cancer specific vesicles or vesicles comprising one or more gastric cancer specific biomarkers, such as listed in FIG. 54.

One or more gastric cancer specific biomarkers, such as listed in FIG. 54, can also be detected by one or more systems disclosed herein, for characterizing a gastric cancer. For example, a detection system can comprise one or more probes to detect one or more gastric cancer specific biomarkers, such as listed in FIG. 54, of one or more vesicles of a biological sample.

Autism

Autism specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 55, and can be used to create an autism specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-484, miR-21, miR-212, miR-23a, miR-598, miR-95, miR-129, miR-431, miR-7, miR-15a, miR-27a, miR-15b, miR-148b, miR-132, or miR-128, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-93, miR-106a, miR-539, miR-652, miR-550, miR-432, miR-193b, miR-181d, miR-146b, miR-140, miR-381, miR-320a, or miR-106b, or any combination thereof. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, GM1, GD1a, GD1b, or GT1b, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more autism specific biomarkers, such as listed in FIG. 55 and in FIG. 1 for autism. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more autism specific biomarkers, such as listed in FIG. 55 and in FIG. 1 for autism. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for autism specific vesicles or vesicles comprising one or more autism specific biomarkers, such as listed in FIG. 55 and in FIG. 1 for autism.

One or more autism specific biomarkers, such as listed in FIG. 55 and in FIG. 1 for autism, can also be detected by one or more systems disclosed herein, for characterizing a autism. For example, a detection system can comprise one or more probes to detect one or more autism specific biomarkers, such as listed in FIG. 55 and in FIG. 1 for autism, of one or more vesicles of a biological sample.

Organ Rejection

Organ rejection specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 56, and can be used to create an organ rejection specific biosignature. For example, the biosignature can comprise one or more overexpressed miRs, such as, but not limited to, miR-658, miR-125a, miR-320, miR-381, miR-628, miR-602, miR-629, or miR-125a, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such as, but not limited to, miR-324-3p, miR-611, miR-654, miR-330_MM1, miR-524, miR-17-3p_MM1, miR-483, miR-663, miR-516-5p, miR-326, miR-197_MM2, or miR-346, or any combination thereof. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, matix metalloprotein-9, proteinase 3, or HNP, or any combinations thereof. The biomarker can be a member of the matrix metalloproteinases.

The invention also provides an isolated vesicle comprising one or more organ rejection specific biomarkers, such as listed in FIG. 56. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more organ rejection specific biomarkers, such as listed in FIG. 56. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for organ rejection specific vesicles or vesicles comprising one or more organ rejection specific biomarkers, such as listed in FIG. 56.

One or more organ rejection specific biomarkers, such as listed in FIG. 56, can also be detected by one or more systems disclosed herein, for characterizing a organ rejection. For example, a detection system can comprise one or more probes to detect one or more organ rejection specific biomarkers, such as listed in FIG. 56, of one or more vesicles of a biological sample.

Methicillin-Resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 57, and can be used to create a methicillin-resistant Staphylococcus aureus specific biosignature.

The one or more mRNAs that may be analyzed include, but are not limited to, TSST-1 which can be used as a specific biomarker from a vesicle for methicillin-resistant Staphylococcus aureus. A biomarker mutation for methicillin-resistant Staphylococcus aureus that can be assessed in a vesicle includes, but is not limited to, a mutation of mecA, Protein A SNPs, or any combination of mutations specific for methicillin-resistant Staphylococcus aureus. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, ETA, ETB, TSST-1, or leukocidins, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more methicillin-resistant Staphylococcus aureus specific biomarkers, such as listed in FIG. 57. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more methicillin-resistant Staphylococcus aureus specific biomarkers, such as listed in FIG. 57. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for methicillin-resistant Staphylococcus aureus specific vesicles or vesicles comprising one or more methicillin-resistant Staphylococcus aureus specific biomarkers, such as listed in FIG. 57.

One or more methicillin-resistant Staphylococcus aureus specific biomarkers, such as listed in FIG. 57, can also be detected by one or more systems disclosed herein, for characterizing a methicillin-resistant Staphylococcus aureus. For example, a detection system can comprise one or more probes to detect one or more methicillin-resistant Staphylococcus aureus specific biomarkers, such as listed in FIG. 57, of one or more vesicles of a biological sample.

Vulnerable Plaque

Vulnerable plaque specific biomarkers from a vesicle can include one or more (for example, 2, 3, 4, 5, 6, 7, 8, or more) overexpressed miRs, underexpressed miRs, mRNAs, genetic mutations, proteins, ligands, peptides, snoRNA, or any combination thereof, such as listed in FIG. 58, and can be used to create a vulnerable plaque specific biosignature. The protein, ligand, or peptide that can be assessed in a vesicle can include, but is not limited to, IL-6, MMP-9, PAPP-A, D-dimer, fibrinogen, Lp-PLA2, SCD40L, Il-18, oxLDL, GPx-1, MCP-1, P1GF, or CRP, or any combination thereof.

The invention also provides an isolated vesicle comprising one or more vulnerable plaque specific biomarkers, such as listed in FIG. 58 and in FIG. 1 for vulnerable plaque. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more vulnerable plaque specific biomarkers, such as listed in FIG. 58 and in FIG. 1 for vulnerable plaque. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vulnerable plaque specific vesicles or vesicles comprising one or more vulnerable plaque specific biomarkers, such as listed in FIG. 58 and in FIG. 1 for vulnerable plaque.

One or more vulnerable plaque specific biomarkers, such as listed in FIG. 58 and in FIG. 1 for vulnerable plaque, can also be detected by one or more systems disclosed herein, for characterizing a vulnerable plaque. For example, a detection system can comprise one or more probes to detect one or more vulnerable plaque specific biomarkers, such as listed in FIG. 58 and in FIG. 1 for vulnerable plaque, of one or more vesicles of a biological sample.

Autoimmune Disease

The invention also provides an isolated vesicle comprising one or more autoimmune disease specific biomarkers, such as listed in FIG. 1 for autoimmune disease. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more autoimmune disease specific biomarkers, such as listed in FIG. 1 for autoimmune disease. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for autoimmune disease specific vesicles or vesicles comprising one or more autoimmune disease specific biomarkers, such as listed in FIG. 1 for autoimmune disease.

One or more autoimmune disease specific biomarkers, such as listed in FIG. 1 for autoimmune disease, can also be detected by one or more systems disclosed herein, for characterizing a autoimmune disease. For example, a detection system can comprise one or more probes to detect one or more autoimmune disease specific biomarkers, such as listed in FIG. 1 for autoimmune disease, of one or more vesicles of a biological sample.

Tuberculosis (TB)

The invention also provides an isolated vesicle comprising one or more TB disease specific biomarkers, such as listed in FIG. 1 for TB disease. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more TB disease specific biomarkers, such as listed in FIG. 1 for TB disease. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for TB disease specific vesicles or vesicles comprising one or more TB disease specific biomarkers, such as listed in FIG. 1 for TB disease.

One or more TB disease specific biomarkers, such as listed in FIG. 1 for TB disease, can also be detected by one or more systems disclosed herein, for characterizing a TB disease. For example, a detection system can comprise one or more probes to detect one or more TB disease specific biomarkers, such as listed in FIG. 1 for TB disease, of one or more vesicles of a biological sample.

HIV

The invention also provides an isolated vesicle comprising one or more HIV disease specific biomarkers, such as listed in FIG. 1 for HIV disease. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more HIV disease specific biomarkers, such as listed in FIG. 1 for HIV disease. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for HIV disease specific vesicles or vesicles comprising one or more HIV disease specific biomarkers, such as listed in FIG. 1 for HIV disease.

One or more HIV disease specific biomarkers, such as listed in FIG. 1 for HIV disease, can also be detected by one or more systems disclosed herein, for characterizing a HIV disease. For example, a detection system can comprise one or more probes to detect one or more HIV disease specific biomarkers, such as listed in FIG. 1 for HIV disease, of one or more vesicles of a biological sample.

The one or more biomarker can also be a miRNA, such as an upregulated or overexpressed miRNA. The upregulated miRNA can be miR-29a, miR-29b, miR-149, miR-378 or miR-324-5p. One or more biomarkers can also be used to charcterize HIV-1 latency, such as by assessing one or more miRNAs. The miRNA can be miR-28, miR-125b, miR-150, miR-223 and miR-382, and upregulated.

Asthma

The invention also provides an isolated vesicle comprising one or more asthma disease specific biomarkers, such as listed in FIG. 1 for asthma disease. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more asthma disease specific biomarkers, such as listed in FIG. 1 for asthma disease. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for asthma disease specific vesicles or vesicles comprising one or more asthma disease specific biomarkers, such as listed in FIG. 1 for asthma disease.

One or more asthma disease specific biomarkers, such as listed in FIG. 1 for asthma disease, can also be detected by one or more systems disclosed herein, for characterizing a asthma disease. For example, a detection system can comprise one or more probes to detect one or more asthma disease specific biomarkers, such as listed in FIG. 1 for asthma disease, of one or more vesicles of a biological sample.

Lupus

The invention also provides an isolated vesicle comprising one or more lupus disease specific biomarkers, such as listed in FIG. 1 for lupus disease. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more lupus disease specific biomarkers, such as listed in FIG. 1 for lupus disease. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for lupus disease specific vesicles or vesicles comprising one or more lupus disease specific biomarkers, such as listed in FIG. 1 for lupus disease.

One or more lupus disease specific biomarkers, such as listed in FIG. 1 for lupus disease, can also be detected by one or more systems disclosed herein, for characterizing a lupus disease. For example, a detection system can comprise one or more probes to detect one or more lupus disease specific biomarkers, such as listed in FIG. 1 for lupus disease, of one or more vesicles of a biological sample.

Influenza

The invention also provides an isolated vesicle comprising one or more influenza disease specific biomarkers, such as listed in FIG. 1 for influenza disease. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more influenza disease specific biomarkers, such as listed in FIG. 1 for influenza disease. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for influenza disease specific vesicles or vesicles comprising one or more influenza disease specific biomarkers, such as listed in FIG. 1 for influenza disease.

One or more influenza disease specific biomarkers, such as listed in FIG. 1 for influenza disease, can also be detected by one or more systems disclosed herein, for characterizing a influenza disease. For example, a detection system can comprise one or more probes to detect one or more influenza disease specific biomarkers, such as listed in FIG. 1 for influenza disease, of one or more vesicles of a biological sample.

Thyroid Cancer

The invention also provides an isolated vesicle comprising one or more thyroid cancer specific biomarkers, such as AKAP9-BRAF, CCDC6-RET, ERC1-RETM, GOLGA5-RET, HOOK3-RET, HRH4-RET, KTN1-RET, NCOA4-RET, PCM1-RET, PRKARA1A-RET, RFG-RET, RFG9-RET, Ria-RET, TGF-NTRK1, TPM3-NTRK1, TPM3-TPR, TPR-MET, TPR-NTRK1, TRIM24-RET, TRIM27-RET or TRIM33-RET, characteristic of papillary thyroid carcinoma; or PAX8-PPARy, characteristic of follicular thyroid cancer. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more thyroid cancer specific biomarkers, such as listed in FIG. 1 for thyroid cancer. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for thyroid cancer specific vesicles or vesicles comprising one or more thyroid cancer specific biomarkers, such as listed in FIG. 1 for thyroid cancer.

One or more thyroid cancer specific biomarkers, such as listed in FIG. 1 for thyroid cancer, can also be detected by one or more systems disclosed herein, for characterizing a thyroid cancer. For example, a detection system can comprise one or more probes to detect one or more thyroid cancer specific biomarkers, such as listed in FIG. 1 for thyroid cancer, of one or more vesicles of a biological sample.

Gene Fusions

The one or more biomarkers assessed of vesicle, can be a gene fusion, such as one or more listed in FIG. 59. A fusion gene is a hybrid gene created by the juxtaposition of two previously separate genes. This can occur by chromosomal translocation or inversion, deletion or via trans-splicing. The resulting fusion gene can cause abnormal temporal and spatial expression of genes, such as leading to abnormal expression of cell growth factors, angiogenesis factors, tumor promoters or other factors contributing to the neoplastic transformation of the cell and the creation of a tumor. Such fusion genes can be oncogenic due to the juxtaposition of: 1) a strong promoter region of one gene next to the coding region of a cell growth factor, tumor promoter or other gene promoting oncogenesis leading to elevated gene expression, or 2) due to the fusion of coding regions of two different genes, giving rise to a chimeric gene and thus a chimeric protein with abnormal activity.

An example of a fusion gene is BCR-ABL, a characteristic molecular aberration in ˜90% of chronic myelogenous leukemia (CML) and in a subset of acute leukemias (Kurzrock et al., Annals of Internal Medicine 2003; 138(10):819-830). The BCR-ABL results from a translocation between chromosomes 9 and 22. The translocation brings together the 5′ region of the BCR gene and the 3′ region of ABL1, generating a chimeric BCR-ABL1 gene, which encodes a protein with constitutively active tyrosine kinase activity (Mittleman et al., Nature Reviews Cancer 2007; 7(4):233-245). The aberrant tyrosine kinase activity leads to de-regulated cell signaling, cell growth and cell survival, apoptosis resistance and growth factor independence, all of which contribute to the pathophysiology of leukemia (Kurzrock et al., Annals of Internal Medicine 2003; 138(10):819-830).

Another fusion gene is IGH-MYC, a defining feature of ˜80% of Burkitt's lymphoma (Ferry et al. Oncologist 2006; 11(4):375-83). The causal event for this is a translocation between chromosomes 8 and 14, bringing the c-Myc oncogene adjacent to the strong promoter of the immunoglobin heavy chain gene, causing c-myc overexpression (Mittleman et al., Nature Reviews Cancer 2007; 7(4):233-245). The c-myc rearrangement is a pivotal event in lymphomagenesis as it results in a perpetually proliferative state. It has wide ranging effects on progression through the cell cycle, cellular differentiation, apoptosis, and cell adhesion (Ferry et al. Oncologist 2006; 11(4):375-83).

A number of recurrent fusion genes have been catalogued in the Mittleman database (cgap.nci.nih.gov/Chromosomes/Mitelman) and can be assessed in a vesicle, and used to characterize a phenotype. The gene fusion can be used to characterize a hematological malignancy or epithelial tumor. For example, TMPRSS2-ERG, TMPRSS2-ETV and SLC45A3-ELK4 fusions can be detected and used to characterize prostate cancer; and ETV6-NTRK3 and ODZ4-NRG1 for breast cancer.

Assessing the presence or absence, or expression level of a fusion gene can be used to diagnosis a phenotype such as a cancer as well as for monitoring therapeutic response to a treatment. For example, the presence of the BCR-ABL fusion gene is a characteristic not only for the diagnosis of CML, but it is also the target of the drug imatinib mesylate (Gleevec, Novartis), a receptor tyrosine kinase inhibitor, for the treatment of CML. Imatinib treatment has led to molecular responses (disappearance of BCR-ABL+ blood cells) and improved progression-free survival in BCR-ABL+CML patients (Kantarjian et al., Clinical Cancer Research 2007; 13(4):1089-1097).

In some embodiments, a heterogeneous population of vesicles is assessed for the presence, absence, or expression level of the gene fusion. In other embodiments, vesicles that are assessed are derived from a specific cell type, such as cell-of-origin specific vesicle, as described herein. Exemplary fusion proteins that can play a role in creating a biosignature are outlined below. One of skill will understand that additional fusions, including those yet to be identified to date, can be used to create a biosignature once their presence is correlated with a vesicle of interest, e.g., a vesicle associated with a given disease.

Breast Cancer

To characterize a breast cancer, a vesicle can be assessed for one or more breast cancer specific fusions, including, but not limited to, ETV6-NTRK3. The vesicle can be derived from a breast cancer cell.

Lung Cancer

To characterize a lung cancer, a vesicle can be assessed for one or more lung cancer specific fusions, including, but not limited to, RLF-MYCL1, TGF-ALK, or CD74-ROS1. The vesicle can be derived from a lung cancer cell.

Prostate Cancer

To characterize a prostate cancer, a vesicle can be assessed for one or more prostate cancer specific fusions, including, but not limited to, ACSL3-ETV1, C150RF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4. The vesicle can be derived from a prostate cancer cell.

Brain Cancer

To characterize a brain cancer, a vesicle can be assessed for one or more brain cancer specific fusions, including, but not limited to, GOPC-ROS1. The vesicle can be derived from a brain cancer cell.

Head and Neck Cancer

To characterize a head and neck cancer, a vesicle can be assessed for one or more head and neck cancer specific fusions, including, but not limited to, CHCHD7-PLAG1, CTNNB1-PLAG1, FHIT-HMGA2, HMGA2-NFIB, LIFR-PLAG1, or TCEA1-PLAG1. The vesicle can be derived from a head and/or neck cancer cell.

Renal Cell Carcinoma (RCC)

To characterize a RCC, a vesicle can be assessed for one or more RCC specific fusions, including, but not limited to, ALPHA-TFEB, NONO-TFE3, PRCC-TFE3, SFPQ-TFE3, CLTC-TFE3, or MALAT1-TFEB. The vesicle can be derived from a RCC cell.

Thyroid Cancer

To characterize a thyroid cancer, a vesicle can be assessed for one or more thyroid cancer specific fusions, including, but not limited to, AKAP9-BRAF, CCDC6-RET, ERC1-RETM, GOLGA5-RET, HOOK3-RET, HRH4-RET, KTN1-RET, NCOA4-RET, PCM1-RET, PRKARA1A-RET, RFG-RET, RFG9-RET, Ria-RET, TGF-NTRK1, TPM3-NTRK1, TPM3-TPR, TPR-MET, TPR-NTRK1, TRIM24-RET, TRIM27-RET or TRIM33-RET, characteristic of papillary thyroid carcinoma; or PAX8-PPARy, characteristic of follicular thyroid cancer. The vesicle can be derived from a thyroid cancer cell.

Blood Cancers

To characterize a blood cancer, a vesicle can be assessed for one or more blood cancer specific fusions, including, but not limited to, TTL-ETV6, CDK6-MLL, CDK6-TLX3, ETV6-FLT3, ETV6-RUNX1, ETV6-TTL, MLL-AFF1, MLL-AFF3, MLL-AFF4, MLL-GAS7, TCBA1-ETV6, TCF3-PBX1 or TCF3-TFPT, characteristic of acute lymphocytic leukemia (ALL); BCL11B-TLX3, IL2-TNFRFS17, NUP214-ABL1, NUP98-CCDC28A, TAL1-STIL, or ETV6-ABL2, characteristic of T-cell acute lymphocytic leukemia (T-ALL); ATIC-ALK, KIAA1618-ALK, MSN-ALK, MYH9-ALK, NPM1-ALK, TGF-ALK or TPM3-ALK, characteristic of anaplastic large cell lymphoma (ALCL); BCR-ABL1, BCR-JAK2, ETV6-EVI1, ETV6-MN1 or ETV6-TCBA1, characteristic of chronic myelogenous leukemia (CML); CBFB-MYH11, CHIC2-ETV6, ETV6-ABL1, ETV6-ABL2, ETV6-ARNT, ETV6-CDX2, ETV6-HLXB9, ETV6-PER1, MEF2D-DAZAP1, AML-AFF1, MLL-ARHGAP26, MLL-ARHGEF12, MLL-CASC5, MLL-CBL, MLL-CREBBP, MLL-DAB21P, MLL-ELL, MLL-EP300, MLL-EPS15, MLL-FNBP1, MLL-FOXO3A, MLL-GMPS, MLL-GPHN, MLL-MLLT1, MLL-MLLT11, MLL-MLLT3, MLL-MLLT6, MLL-MYO1F, MLL-PICALM, MLL-SEPT2, MLL-SEPT6, MLL-SORBS2, MYST3-SORBS2, MYST-CREBBP, NPM1-MLF1, NUP98-HOXA13, PRDM16-EVI1, RABEP1-PDGFRB, RUNX1-EVI1, RUNX1-MDS1, RUNX1-RPL22, RUNX1-RUNX1T1, RUNX1-SH3D19, RUNX1-USP42, RUNX1-YTHDF2, RUNX1-ZNF687, or TAF15-ZNF-384, characteristic of AML; CCND1-FSTL3, characteristic of chronic lymphocytic leukemia (CLL); BCL3-MYC, MYC-BTG1, BCL7A-MYC, BRWD3-ARHGAP20 or BTG1-MYC, characteristic of B-cell chronic lymphocytic leukemia (B-CLL); CITTA-BCL6, CLTC-ALK, IL21R-BCL6, PIM1-BCL6, TFCR-BCL6, IKZF1-BCL6 or SEC31A-ALK, characteristic of diffuse large B-cell lymphomas (DLBCL); FLIP1-PDGFRA, FLT3-ETV6, KIAA1509-PDGFRA, PDE4DIP-PDGFRB, NIN-PDGFRB, TP53BP1-PDGFRB, or TPM3-PDGFRB, characteristic of hyper eosinophilia/chronic eosinophilia; IGH-MYC or LCP1-BCL6, characteristic of Burkitt's lymphoma. The vesicle can be derived from a blood cancer cell.

The invention also provides an isolated vesicle comprising one or more gene fusions as disclosed herein, such as listed in FIG. 59. A composition comprising the isolated vesicle is also provided. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more gene fusions, such as listed in FIG. 59. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more gene fusions of interest, such as listed in FIG. 59.

Also provided herein is a detection system for detecting one or more gene fusions, such as gene fusions listed in FIG. 59. For example, a detection system can comprise one or more probes to detect one or more gene fusions of interest. Detection of the one or more gene fusions can be used to characterize a phenotype according to the invention. In some embodiments, mRNA corresponding to a gene-fusion is found within the payload of a vesicle. In some embodiments, the fusion gene product, e.g., a protein fusion, is detected.

Gene-Associated Biomarkers

The one or more biomarkers assessed according to the methods of the invention can also include one or more genes selected from the group consisting of PFKFB3, RHAMM (HMMR), cDNA FLJ42103, ASPM, CENPF, NCAPG, Androgen Receptor, EGFR, HSP90, SPARC, DNMT3B, GART, MGMT, SSTR3, and TOP2B. A microRNA that interacts with the one or more genes can also be a biomarker (see for example, FIG. 60). In some embodiments, the one or more biomarkers are used to characterize a disease, e.g., a cancer such as prostate cancer.

The invention also provides an isolated vesicle comprising one or more one or more biomarkers selected from the group consisting of PFKFB3, RHAMM (HMMR), cDNA FLJ42103, ASPM, CENPF, NCAPG, Androgen Receptor, EGFR, HSP90, SPARC, DNMT3B, GART, MGMT, SSTR3, and TOP2B; or the microRNA that interacts with these biomarkers (see for example, FIG. 60). In some embodiments, the invention provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of PFKFB3, RHAMM (HMMR), cDNA FLJ42103, ASPM, CENPF, NCAPG, Androgen Receptor, EGFR, HSP90, SPARC, DNMT3B, GART, MGMT, SSTR3, and/or TOP2B; or the microRNA that interacts with the one or more genes, such as listed in FIG. 60. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more biomarkers consisting of PFKFB3, RHAMM (HMMR), cDNA FLJ42103, ASPM, CENPF, NCAPG, Androgen Receptor, EGFR, HSP90, SPARC, DNMT3B, GART, MGMT, SSTR3, and TOP2B; or the microRNA that interacts with the one or more genes, such as listed in FIG. 60.

One or more prostate cancer specific biomarkers, such as listed in FIG. 60 can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more prostate cancer specific biomarkers, such as listed in FIG. 60, of one or more vesicles of a biological sample.

In some embodiments, the one or more biomarker for characterizing a cancer is TBP; ILT.2; ABCC5; CD18; GATA3; DICER1; MSH3; GBP1; IRS1; CD3z; fas1; TUBB; BAD; ERCC1; MCM6; PR; APC; GGPS1; KRT18; ESRRG; E2F1; AKT2; A.Catenin; CEGP1; NPD009; MAPK14; RUNX1; ID2; G.Catenin; FBXO5; FHIT; MTA1; ERBB4; FUS; BBC3; IGF1R; CD9; TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; CDC20; STAT3; ERK1; HLA.DPB1; SGCB; CGA; DHPS; MGMT; CRTP2; MMP12; ErbB3; RAP1GDS1; CDC25B; IL6; CCND1; CYBA; PRKCD; DR4; Hepsin; CRABP1; AK055699; Contig.51037; VCAM1; FYN; GRB7; AKAP.2; RASSF1; MCP1; ZNF38; MCM2; GBP2; SEMA3F; CD31; COL1A1; ER2; BAG1; AKT1; COL1A2; STAT1; Wnt.5a; PTPD1; RAB6C; TK1, ErbB2, CCNB1, BIRC5, STK6, MKI67, MYBL2, MMP1 1, CTSL2, CD68, GSTM1, BCL2, ESR1, or a combination thereof. The biomarker can be an RNA level or transcript or other gene expression product, such as described in PCT Publication No. WO2005100606, which is incorporated by reference in its entirety herein.

In one embodiment, for every unit of increased expression of one or more of ILT.2; CD18; GBP1; CD3z; fas1; MCM6; E2F1; ID2; FBXO5; CDC20; HLA.DPB1; CGA; MMP12; CDC25B; IL6; CYBA; DR4; CRABP1; Contig.51037; VCAM1; FYN; GRB7; AKAP.2; RASSF1; MCP1; MCM2; GBP2; CD31; ER2; STAT1; TK1; ERBB2, CCNB1, BIRC5, STK6, MKI67, MYBL2, MMP1 1, CTSL2, CD68, or a combination thereof, a subject is predicted to have an increased likelihood of response to chemotherapy.

In another embodiment, every unit of increased expression of one or more of TBP; ABCC5; GATA3; DICER1; MSH3; IRS1; TUBB; BAD; ERCC1; PR; APC; GGPS1; KRT18; ESRRG; AKT2; A.Catenin; CEGP1; NPD009; MAPK14; RUNX1; G.Catenin; FHIT; MTA1; ErbB4; FUS; BBC3; IGF1R; CD9; TP53BP1; MUC1; IGFBP5; rhoC; RALBP1; STAT3; ERK1; SGCB; DHPS; MGMT; CRIP2; ErbB3; RAP1GDS1; CCND1; PRKCD; Hepsin; AK055699; ZNF38; SEMA3F; COL1A1; BAG1; AKT1; COL1A2; Wnt.5a; PTPD1; RAB6C; GSTM1, BCL2, ESR1, or a combination thereof, a subject is predicted to have a decreased likelihood of response to chemotherapy.

In some embodiments, the one or more biomarker for characterizing a cancer is BCatenin; BAG1, BIN1, BUB1, C20_orf1, CCNB1, CCNE2; CDC20; CDH1; CEGP1, CIAP1, cMYC, CTSL2; DKFZp586M07, DR5, EpCAM, EstR1; FOXM1; GRB7; GSTM1; GSTM3; HER2; HNRPAB, ID1, IGF1R, ITGA7; Ki_(—)67, KNSL2, LMNB1, MCM2; MELK; MMP12; MMP9, MYBL2; NEK2; NME1, NPD009, PCNA; PR; PREP; PTTG1; RPLPO; Src, STK15; STMY3; SURV; TFRC; TOP2A; TS, or a combination thereof. The biomarker can be an RNA level or transcript or other gene expression product, such as described in PCT Publication No. WO2005039382, which is herein incorporated by reference in its entirety.

In one embodiment, expression of one or more of BUB1, C20orf1, CCNB1, CCNE2, CDC20, CDH1, CTSL2, EpCAM, FOXM1, GRB7, HER2, HNRPAB, Ka 67, KNSL2, LMNB1, MCM2, MELK, MMP12, MMP9, MYBL2, NEK2, NME1, PCNA, PREP, PTTG1, Src, STK15, STMY3, SURV, TFRC, TOP2A, TS, or a combination thereof indicates a decreased likelihood of long-term survival without cancer recurrence. In another embodiment, expression of one or more of BUB1, C20orf1, CCNB1, CCNE2, CDC20, CDH1, CTSL2, EpCAM, FOXM1, GRB7, HER2, HNRPAB, Ka 67, KNSL2, LMNB1, MCM2, MELK, MMP12, MMP9, MYBL2, NEK2, NME1, PCNA, PREP, PTTG1, Src, STK15, STMY3, SURV, TFRC, TOP2A, TS, or a combination thereof indicates a decreased likelihood of long-term survival without cancer recurrence. In yet another embodiment, expression of one or more of BAG1, BCatenin, BIN1, CEGP1, CIAP1, cMYC, DKFZp586M07, DR5, EstR1, GSTM1, GSTM3, ID1, IGF1R, ITGA7, NPD009, PR, RPLPO, or a combination thereof, indicates an increased likelihood of long-term survival without cancer recurrence. In some embodiment, the cancer is breast cancer.

In some embodiments, the one or more biomarker for characterizing a cancer is p53BP2, cathepsin B, cathepsin L₅ Ki67/MiB1, thymidine kinase, or a combination thereof. In one embodiment, the one or more biomarkers is normalized against a control gene or genes, and compared to the amount found in a reference cancer tissue set, wherein a poor outcome is predicted if: (a) the expression level of p53BP2 is in the lower 10^(th) percentile; or (b) the expression level of either cathepsin B or cathepsin L is in the upper 10^(th) percentile; or (c) the expression level of any either Ki67/MiB1 or thymidine kinase is in the upper 10^(th) percentile, such as described in PCT Publication No. WO2003078662, which is herein incorporated by reference in its entirety. In some embodiments, the poor outcome is a clinical outcome as measured in terms of shortened survival or increased risk of cancer recurrence. In another embodiment, the poor clinical outcome is measured in terms of shortened survival or increased risk of cancer recurrence following surgical removal of the cancer.

In some embodiments, the one or more biomarker for characterizing a cancer is Bcl2, hepatocyte nuclear factor 3, ER, ErbB2 or Grb7. In one embodiment, the one or more biomarker (e.g. RNA or their expression products) is normalized against a control gene or genes, and compared to the amount found in a reference cancer tissue set, wherein (i) tumors expressing at least one of Bcl2, hepatocyte nuclear factor 3, and ER, or their expression products, above the mean expression level in the reference tissue set are classified as having a good prognosis for disease free and overall patient survival following treatment; and (ii) tumors expressing elevated levels of ErbB2 and Grb7, or their expression products, at levels ten-fold or more above the mean expression level in the reference tissue set are classified as having poor prognosis of disease free and overall patient survival following treatment, such as described in PCT Publication No. WO2003078662, which is herein incorporated by reference in its entirety.

In another embodiment, the one or more biomarkers is FOXM1, PRAME, Bcl2, STK15, CEGP1, Ki-67, GSTM1, CA9, PR, BBC3, NME1, SURV, GATA3, TFRC, YB-I, DPYD, GSTM3, RPS6 KB1, Src, Chk1, ID1, EstR1, p27, CCNB1, XIAP, Chk2, CDC25B, IGF1R, AK055699, P13KC2A, TGFB3, BAGI1, CYP3A4, EpCAM, VEGFC, pS2, hENT1, W1SP1, HNF3A, NFKBp65, BRCA2, EGFR, TK1, VDR, Contig51037, pENT1, EPHX1, IF1A, DIABLO, CDH1, HIF1 α, IGFBP3, CTSB, Her2, or a combination thereof. In one embodiment, overexpression of one or more of FOXM1, PRAME, STK15, Ki-67, CA9, NME1, SURV, TFRC, YB-I, RPS6 KB1, Src, Chk1, CCNB1, Chk2, CDC25B, CYP3A4, EpCAM, VEGFC, hENT1, BRCA2, EGFR, TK1, VDR, EPHX1, IF1A, Contig51037, CDH1, HIF1 α, IGFBP3, CTSB, Her2, pENT1, or a combination thereof, indicates a decreased likelihood of long-term survival without breast cancer recurrence. In another embodiment, overexpression of one or more of Bcl2, CEGP1, GSTM1, PR, BBC3, GATA3, DPYD, GSTM3, ID1, EstR1, p27, XIAP, IGF1R, AK055699, P13KC2A, TGFB3, BAGI1, pS2, WISP1, HNF3A, NFKBp65, DIABLO, or a combination thereof indicates an increased likelihood of long-term survival without breast cancer recurrence, such as described in PCT Publication No. WO2003078662, which is herein incorporated by reference in its entirety.

In another embodiment, the one or more biomarker for characterizing a cancer is ABCC1, ABCC5, ABCD1, ACTB, ACTR2, AKT1, AKT2, APC, APOC1, APOE, APRT, BAK1, BAX, BBC3, BCL2.mu.1, BCL2L13, BID, BUB1, BUB3, CAPZA1, CCT3, CD14, CDC25B, CDCA8, CHEK2, CHFR, CSNK1D, CST7, CXCR4, DDR1, DICER1, DUSP1, ECGF1, E1F4E2, ERBB4, ESR1, FAS, GADD45B, GATA3, GCLC, GDF15, GNS, HDAC6, HSPA1A, HSPA1B, HSPA9B, IL7, ILK, LAPTM4B, LILRB1, LIMK2, MAD2L1BP, MAP2K3, MAPK3, MAPRE1, MCL1, MRE11A, NEK2, NFKB1, NME6, NTSR2, PLAU, PLD3, PPP2CA, PRDX1, PRKCH, RAD1, RASSF1, RCC1, REG1A, RELA, RHOA, RHOB, RPN2, RXRA, SHC1, SIRT1, SLC1A3, SLC35B1, SRC, STK10, STMN1, TBCC, TBCD, TNFRSF10A, TOP3B, TSPAN4, TUBA3, TUBA6, TUBB, TUBB2C, UFM1, VEGF, VEGFB, VHL, ZW10, ZWILCH, or a combination thereof, such as for a hormone receptor (HR) positive cancer patient, as described in US Patent Application Publication No. US20090311702, which is herein incorporated by reference in its entirety.

In one embodiment, the expression level is used to determine a likelihood of a beneficial response to a treatment including a taxane for a hormone receptor (HR) positive cancer patient, wherein expression of DDR1, E1F4E2, TBCC, STK10, ZW10, BBC3, BAX, BAK1, TSPAN4, SLC1A3, SHC1, CHFR, RHOB, TUBA6, BCL2L13, MAPRE1, GADD45B, HSPA1B, FAS, TUBB, HSPA1A, MCL1, CCT3, VEGF, TUBB2C, AKT1, MAD2L1BP, RPN2, RHOA, MAP2K3, BID, APOE, ESR1, ILK, NTSR2, TOP3B, PLD3, DICER1, VHL, GCLC, RAD1, GATA3, CXCR4, NME6, UFM1, BUB3, CD14, MRE11A, CST7, APOC1, GNS, ABCC5, AKT2, APRT, PLAU, RCC1, CAPZA1, RELA, NFKB1, RASSF1, BCL2L11, CSNK1D, SRC, LIMK2, SIRT1, RXRA, ABCD1, MAPK3, DUSP1, ABCC1, PRKCH, PRDX1, TUBA3, VEGFB, LILRB1, LAPTM4B, HSPA9B, ECGF1, GDF15, ACTR2, IL7, HDAC6, CHEK2, REG1A, APC, SLC35B1, ACTB, PPP2CA, TNFRSF10A, TBCD, ERBB4, CDC25B, STMN1, or a combination thereof is positively correlated with increased likelihood of a beneficial response to a treatment including a taxane. In another embodiment, expression of CDCA8, ZWILCH, NEK2, BUB1, or a combination thereof is negatively correlated with an increased likelihood of a beneficial response to a treatment including a taxane.

In another embodiment, the one or more biomarker for characterizing a cancer for a hormone receptor (HR) positive cancer patient is ABCA9, ABCC1, ABCC10, ABCC3, ABCD1, ACTB, ACTR2, ACTR3, AKT1, AKT2, APC, APEX1, APOC1, APOE, APRT, BAD, BAK1, BAX, BBC3, BCL2, BCL2L1, BCL2L11, BCL2L13, BID, BIRC3, BIRC4, BUB3, CAPZA1, CCT3, CD14, CD247, CD63, CD68, CDC25B, CHEK2, CHFR, CHGA, COL1A1, COL6A3, CRABP1, CSNK1D, CST7, CTSD, CXCR4, CYBA, CYP1B1, DDR1, DIABLO, DICER1, DUSP1, ECGF1, E1F4E2, ELP3, ERBB4, ERCC1, ESR1, FAS, FLAD1, FOS, FOXA1, FUS, FYN, GADD45B, GATA3, GBP1, GBP2, GCLC, GGPS1, GNS, GPX1, HDAC6, HRAS, HSPA1A, HSPA1B, HSPA5, HSPA9B, IGFBP2, IL2RA, IL7, ILK, KDR, KNS2, LAPTM4B, LILRB1, LIMK1, LIMK2, MAD1L1, MAD2L1BP, MAD2L2, MAP2K3, MAP4, MAPK14, MAPK3, MAPRE1, MCL1, MGC52057, MGMT, MMP11, MRE11A, MSH3, NFKB1, NME6, NPC2, NTSR2, PDGFRB, PECAM1, PIK3C2A, PLAU, PLD3, PMS1, PPP2CA, PRDX1, PRKCD, PRKCH, PTEN, PTPN21, RAB6C, RAD1, RASSF1, RB1, RBM17, RCC1, REG1A, RELA, RHOA, RHOB, RHOC, RPN2, RXRA, RXRB, SEC61A1, SGK, SHC1, SIRT1, SLC1A3, SLC35B1, SOD1, SRC, STAT1, STAT3, STK10, STK11, STMN1, TBCC, TBCD, TBCE, TFF1, TNFRSF10A, TNFRSF10B, TOP3B, TP53BP1, TSPAN4, TUBA3, TUBA6, TUBB, TUBB2C, TUBD1, UFM1, VEGF, VEGFB, VEGFC, VHL, XIST, ZW10, WILCH, or a combination thereof.

In one embodiment, the one or more of the biomarkers are selected from the group consisting of: DDR1, ZW10, RELA, BAX, RHOB, TSPAN4, BBC3, SHC1, CAPZA1, STK10, TBCC, E1F4E2, MCL1, RASSF1, VEGF, SLC1A3, DICER1, ILK, FAS, RAB6C, ESR1, MRE11A, APOE, BAK1, UFM1, AKT2, SIRT1, BCL2L13, ACTR2, LIMK2, HDAC6, RPN2, PLD3, RHOA, MAPK14, ECGF1, MAPRE1, HSPA1B, GATA3, PPP2CA, ABCD1, MAD2L1BP, VHL, GCLC, ACTB, BCL2L11, PRDX1, LILRB1, GNS, CHFR, CD68, LIMK1, GADD45B, VEGFB, APRT, MAP2K3, MGC52057, MAPK3, APC, RAD1, COL6A3, RXRB, CCT3, ABCC3, GPX1, TUBB2C, HSPA1A, AKT1, TUBA6, TOP3B, CSNK1D, SOD1, BUB3, MAP4, NFKB1, SEC61A1, MAD1L1, PRKCH, RXRA, PLAU, CD63, CD14, RHOC, STAT1, NPC2, NME6, PDGFRB, MGMT1, GBP1, ERCC1, RCC1, FUS, TUBA3, CHEK2, APOC1, ABCC10, SRC, TUBB, FLAD1, MAD2L2, LAPTM4B, REG1A, PRKCD, CST7, IGFBP2, FYN, KDR, STMN1, RBM17, TP53BP1, CD247, ABCA9, NTSR2, FOS, TNFRSF10A, MSH3, PTEN, GBP2, STK11, ERBB4, TFF1, ABCC1, IL7, CDC25B, TUBD1, BIRC4, ACTR3, SLC35B1, COL1A1, FOXA1, DUSP1, CXCR4, IL2RA, GGPS1, KNS2, RB1, BCL2L1, XIST, BIRC3, BID, BCL2, STAT3, PECAM1, DIABLO, CYBA, TBCE, CYP1B1, APEX1, TBCD, HRAS, TNFRSF10B, ELP3, PIK3C2A, HSPA5, VEGFC, MMP11, SGK, CTSD, BAD, PTPN21, HSPA9B, PMS1, or a combination thereof, is positively correlated with increased likelihood of a beneficial response to a treatment including a taxane. In another embodiment, expression of CHGA, ZWILCH, CRABP1, or a combination thereof is negatively correlated with an increased likelihood of a beneficial response to a treatment including a taxane.

In another embodiment, the one or more biomarkers for characterizing a lung disorder, such as lung cancer, is CYP1B1, AKR1B10, CYP1B1, CYP1A1, CYP1B1, CEACAM5, ALDH3A1, SLC7A11, AKR1C2, NQO1, NQO1, GPX2, MUC5AC, AKR1C2, MUC5AC, AKR1C1, CLDN10, AKR1C3, NQO1, SLC7A11, HGD///LOC642252, AKR1C1, PIR, CYP4F11, TCN1, TM4SF1, KRT14, ME1, CBR1, ADH7, SPDEF, ME1, CXCL14, SRPX2, UPK1B, TRIM16, TRIM16L, LOC653524, KLF4, TXN, TKT, DEFB1, CSTA, CEACAM6, TALDO1, CA12, GCLM, PGD, TXNRD1, CEACAM6, GCLC, GPC1, TFF1, CABYR, CA12, UPK1B, GALNT6, TKT, TSPAN8, UGT1A10, UGT1A8, UGT1A7, UGT1A6, UGT1A, SPDEF, MSMB, ANXA3, MUC5AC, CTGF, IDS, CA12, FTH1, HN1, DPYSL3, GMDS, UGT1A10, UGT1A8, UGT1A7, UGT1A6, UGT1A, ABHD2, GCLC, GALNT7, MSMB, HTATIP2, UGT1A10, UGT1A8, UGT1A7, UGT1A6, UGT1A, S100A10, DAZ1, DAZ3, DAZ2, DAZ4, IDS, PRDX1, CYP4F3, UGT1A10, UGT1A8, UGT1A7, UGT1A6, UGT1A, AGR2, S100P, NDUFA7, MAFG, ZNF323, AP2B1, UGT1A6, NKX3-1, SEPX1, CTSC, GCNT3, GULP1, LOC283677, SMPDL3A, SLC35A3, WBP5, TARS, EIF2AK3, C11orf32, GALNT12, VPS13D, BCL2L13, IMPA2, GMDS, AZGP1, PLCE1, FOLH1, NUDT4, NUDT4P1, TAGLN2, GNE, TSPAN13, GALNT3, HMGN4, SCP2, PLA2G10, GULP1, DIAPH2, RAP1GAP, FTH1, LYPLA1, CREB3L1, AKR1B1, RAB2, SCGB2A1, KIAA0367, ABCC1, TPARL, ABHD2, TSPAN1, DHRS3, ABCC1, FKBP11, TTC9, GSTM3, S100A14, SLC35A1, ENTPD4, P4HB, AGTPBP1, NADK, B4GALT5, CCPG1, PTP4A1, DSG2, CCNG2, CPNE3, SEC31L1, SLC3A2, ARPC3, CDCl₄B, SLC17A5, H1ST1H2AC, CBLB, H1ST1H2BK, TOM1L1, TIMP1, ABCB6, GFPT1, TIAM1, SORL1, PAM, NADK, RND3, XPOT, SERINC5, GSN, HIGD1A, PDIA3, C3orf14, PRDX4, RAB7, GPR153, ARL1, IDS, GHITM, RGC32, TMED2, PTS, GTF3C1, IDH1, LAMP2, ACTL6A, RAB11A, COX5A, APLP2, PTK9, UBE2J1, TACSTD2, PSMD14, PDIA4, MTMR6, FA2H, NUDT4, TBC1D16, PIGP, CCDC28A, AACS, CHP, TJP2, EFHD2, KATNB1, SPA17, TPBG, GALNT1, HSP90B1, TMED10, SOD1, BECN1, C14orf1, COPB2, TXNDC5, SSR4, TLE1, TXNL1, LRRC8D, PSMB5, SQSTM1, ETHE1, RPN2, TIPARP, CAP1, LOC92482, FKBP1A, EDEM1, CANX, TMEM59, GUK1, LOC57228, SPINT2, C20orf111, ECOP, JTB, REXO2, UFDIL, DDX17, SSH3, TRIOBP, GGA1, FAM53C, PPP3CC, SFRS14, ACTN1, SPEN, CYP2J2, TLE2, ProSAPiP1, PFTK1, PCDH7, FLNB, SIX2, CD81, ZNF331, AMACR, GNB5, CUGBP1, EDD1, TLR5, MGLL, CHST4, SERPINI2, PPAP2B, BCL11A, STEAP3, SYNGR1, CRYM, RUTBC1, PARVA, NFIB, TCF7L1, MAGI2, CCDC81, COL9A2, CNKSR1, NCOR2, INHBB, PEX14, TSPAN9, RAB6B, GSTM5, FLJ10159, TNS1, MT2A, TNFSF13, TNFSF12-TNFSF13, I-Mar, ELF5, JAG2, FLJ23191, PHGDH, CYP2F1, TNS3, GAS6, CD302, PTPRM, CCND1, TNFSF13, TNFSF12-TNFSF13, ADCY2, CCND2, MT1X, SNED1, SFRS14, ANXA6, HNMT, AK1, EPOR, EPAS1, PDE8B, CYFIP2, SLIT1, ACCN2, KAL1, MTIE, MTIF, HLF, SITPEC, JAG2, HSPA2, LOC650610, KRT15, SORD, ITM2A, PEC1, HPGD, CKB, HLF, CYP2A6, CYP2A7, CYP2A7P1, CYP2A13, C14orf132, MT1G, FGFR3, PROS1, FAM107A, MT1X, FXYD1, MTIF, CX3CL1, CX3CL1, CYP2A6, HLF, SLIT2, BCAM, FMO2, MT1H, FLRT3, PRG2, TMEM45A, MMP10, C3, LOC653879, CYP2W1, FABP6, SCGB1A1, MUC5B, LOC649768, FAM107A, SEC14L3, 210524_x_at, 213169_at, 212126_at, 43511_s_at, 213891_s_at, 212233_at, 217626_at, AACS, ABHD2, ADCY2, ADH7, ALDH3A1, AP2B1, APLP2, ARHE, ARL1, ARPC3, ASM3A, AZGP1, C14orf1, C1orf8, CANX, CAP1, CCND2, CCNG2, CEACAM5, CEACAM6, CHP, CLDN10, COX5A, CPNE3, CPR8, CTSC, CYPIA1, CYP2F1, CYP4F11, CYP4F3, DAZ4, DCL-1, DKFZP434J214, DPYSL3, ERP70, FKBP11, FKBP1A, FLJ13052, FOLH1, FTH1, GALNT1, GALNT12, GALNT3, GALNT7, GCLM, GCNT3, GFPT1, GMDS, GNE, GRP58, GSN, HGD, H1ST1H2BK, HMGN4, HTATIP2, IDS, IMPA2, JTB, KATNB1, KDELR3, KIAA0227, KIAA0367, KIAA0905, KLF4, LAMP2, LOC92689, LRRC5, ME1, MSMB, MTIG, MUC5B, NKX3-1, NQO1, NUDT4, OASIS, P4HB, PDEF, PIR, PLA2G10, PPP3CC, PRDX4, RAB11A, RAB2, RAP1GA1, RGC32, RNP24, S100A10, SCGB2A1, SDR1, SEPX1, SLC17A5, SLC35A1, SLC7A11, TACSTD2, TAGLN2, TCN1, TIMP1, TKT, TM4SF13, TM4SF3, TMP21, TXNDC5, UBE2J1, UGT1A10, UPK1B, CYP1B1, 203369_x_at, CD164, MUC16, MUC4, MUC5AC, CYP2A6, CYP2B7P1, CYP4B1, POR, CYP2F1, DNAI2, DYNLT1, DNALI1, DNAI1, DNAH9, DNAH7, DYNC112, DYNC1H1, DYNLL1, DYNLRB1, ESD, GSTM2, GSTM1, GSTK1, GSTA1, GPX4, GPX1, MGST2, GSTP1, GSS, GSTO1, KRTI9, KRT7, KRT8, KRT18, KRT10, KRT10, KRT17, KRT5, KRT15, MAPIA, MAPRE1, EML2, MAST4, MACF1, ALDH3A1, ALDH1A1, ALDH3B1, ALDH3B1, ALDH3A2, ALDH1L1, ALDH9A1, ALDH2, K-ALPHA-1, TUBB3, TUBGCP2, TBCA, TUBB2A, TUBA4, TUBB2C, TUBA3, TUBA6, K-ALPHA-1, TUBB, TUBA6, TUBA1, TUBB, K-ALPHA-1, 76P, TUBB3, TUBB2C, or a combination thereof, as described in US Patent Application Publication No. US20090061454, which is herein incorporated by reference in its entirety.

In another embodiment, the one or more biomarker for characterizing a breast cancer is Bcl2, wherein overexpression of Bcl2 indicates an increased likelihood of long-term survival without breast cancer recurrence, as described in US Patent Application Publication No. US20070141589, which is herein incorporated by reference in its entirety. In one embodiment, the breast cancer is characterized by overexpression of the estrogen receptor (ER). In another embodiment, the breast cancer is invasive breast carcinoma. In yet another embodiment, the one or more biomarkers is assessed for a subject with surgical removal of the primary tumor.

In another embodiment, the one or more biomarker for characterizing a breast cancer is FOXM1, PRAME, STK15, CEGP1, Ki-67, GSTM1, CA9, PR, BBC3, NME1, SURV, GATA3, TFRC, YB-1, DPYD, GSTM3, RPS6 KB1, Src, Chk1, ID1, EstR1, p27, CCNB1, XIAP, Chk2, CDC25B, IGF1R, AK055699, P13KC2A, TGFB3, BAG1, CYP3A4, EpCAM, VEGFC, pS2, hENT1, WISP1, HNF3A, NFKBp65, BRCA2, EGFR, TK1, VDR, Contig51037, pENT1, EPHX1, IF1A, DIABLO, CDH1, HIF1.alpha., IGFBP3, CTSB, Her2, or a combination thereof. One or more antigens CD9, MIS Rii, ER, CD63, MUC1, HER3, STAT3, VEGFA, BCA, CA125, CD24, EPCAM, and ERB B4 can be used to assess a breast cancer. In one embodiment, overexpression of one or more of FOXM1, PRAME, STK15, Ki-67, CA9, NME1, SURV, TFRC, YB-1, RPS6 KB1, Src, Chk1, CCNB1, Chk2, CDC25B, CYP3A4, EpCAM, VEGFC, hENT1, BRCA2, EGFR, TK1, VDR, EPHX1, IF1A, Contig51037, CDH1, HIF1.alpha., IGFBP3, CTSB, Her2, pENT1, or a combination thereof, indicates a decreased likelihood of long-term survival without breast cancer recurrence. In another embodiment, overexpression of one or more of CEGP1, GSTM1, PR, BBC3, GATA3, DPYD, GSTM3, ID1, EstR1, p27, XIAP, IGF1R, AK055699, P13KC2A, TGFB3, BAG1, pS2, WISP1, HNF3A, NFKBp65, DIABLO, or a combination thereof indicates an increased likelihood of long-term survival without breast cancer recurrence. In one embodiment, the breast cancer is characterized by overexpression of the estrogen receptor (ER). In another embodiment, the breast cancer is invasive breast carcinoma. In yet another embodiment, the one or more biomarkers is assessed for a subject with surgical removal of the primary tumor.

In another embodiment, the one or more biomarkers for characterizing a breast cancer comprise CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR or a combination thereof. The expression level of the markers can be assessed to characterize a breast cancer, such as provide a diagnosis, prognosis, or theranosis, or by identifying the cancer. In one embodiment, the breast cancer is invasive breast carcinoma.

In some embodiments, data obtained from determining the expression level of one or more biomarkers is subjected to statistical analysis, such as by using the Cox Proportional Hazards model.

In another embodiment, the one or more biomarker for characterizing a lung cancer is Satb1, Hspa9a, Hey1, Gas1, Bnip2, Capn2, Anp32a, Ddit3, Ccnb2, Cdkn2d (p19), Prc1, Uck2, Srm, Shmt1, Slc19a1, Npm1, Npm3, No15, Lamr1/Prsa, Arhu (Rhou), Traf4, Adam19, Bmp6, Rbp1, Reck, Ect2, or a combination thereof. such as described in EP Patent Publication No. EP2105511, which is herein incorporated by reference in its entirety.

In one embodiment, the one or more biomarkers for characterizing a lung cancer is Prc1, Klt4, Ect2, Cdc20, Stk6, Nek6, Birc5, Hspa9a, Cideb Pglyrp, Zfp239, Efl5, Uck2, Smarcc1, Arg1, Hk1, Gapd, Suclg2, Tpi, Gnpnat1, Pign, Gapd, Mre11a, Top2a, Ard1, Hmgb2, Xrcc5, Rrm1, Rrm2, Smarcc1, Npm3, No15, Lamr1, H1fx, Lmnb1, Spnr, Npm3, Nola1 Mki67ip, Ppan, Rnac, Grwd1, Srr, Pycs Pcbd, Mrps5, Lamr1, Mrp112, Rp144, Eif2b, Tomm40, Slc15a2, Slc4a7, Slc4a-4, Rangnrf, Kpnb3, Ipo4, M1p, Stk39, Rbp1, Reck, Areg, Ros1, Arhu, Frat2, Traf4, Myc, Frat2, Cldn2, Ghb3, Gja1, Krt1-18, Col15a1, Dsg2, Ect2, Lcn2, Kng, Hgfac, Adora2b, Spint1, Adam19, Hpn, Cdkn2d, Lats2, Hey1, Stat1, Bnip2, capn2, Anp32a, Madh6, Foxf1a, Tbx3, Tcf21, Gata3, Sox2, Crap, Trim30, Klf₇, Sox17, Sox18, Meis1, Foxf2, Satb1, Anp32a, Bmp6, Tgfb1, Dpt, Acvr11, Eng, Zfhx1a, Igfbp6, Igfbp6, Igfbp4, Socs2, Nfkbia, Sox7, Ptpre, Ptpns1, Rassf5, Fkbp7, Sema3f, Vsn11, Reck, Capn2, Cdh5, Spock2, Thbd, Tie1 Icam2, Tek, Nes, Vwf, Xlkd1, Sparcl1, Marcks, Tenc1, Pcdha6, Lama4, Lama3, Pcdha4, Vtn, Vcam1, Tna, Stab1, Pmp22, Ptprb, Ptprg, Slfn2, Ndr2, Ets1, Sipa1, Ndn, Meox2, Rbp1, Sema7a, Sema3c, Sema3e, Tag1n, Ablim1, or a combination thereof.

The lung cancer can be a lung adenocarcinoma, such as bronchiolar alveolar carcinoma (BAC) or papillary lung adenocarcinoma (PLAC).

In one embodiment, characterizing the lung cancer comprises monitoring a subject with lung cancer on a treatment, wherein the treatment comprises irinotecan, paclitaxel, 5-fluorouracil, a drug that binds EpCam (such as an EpCam antibody), or a combination thereof. In another embodiment, characterizing the lung cancer comprises distinguishing between different subtypes of lung cancer. For example, detecting an increased level of Ccnb2, Slc19a1, Uck2, Srm1, No16a, Arhu, Adam19, Ect2, Shmt1, or a combination thereof, such as compared to level of the one or more biomarkers in a control individual, can be indicative of PLAC. In another embodiment, detecting a decreased level of Gas1, Bmp6, Bnip2, Capn2, Ddit3, Hey1 or a combination thereof, such as compared to level of the one or more biomarkers in a control individual, can be indicative of PLAC.

In another embodiment, detecting an increased level of Prc1, Klt4, Ect2, Cdc20, Stk6, Nek6, Birc5, Hspa9a, Cideb Pglyrp, Zfp239, Efl5, Uck2, Smarcc1, Arg1, Hk1, Gapd, Suclg2, Tpi, Gnpnat1, Pign, Gapd, Mre11a, Top2a, Ard1, Hmgb2, Xrcc5, Rrm1, Rrm2, Smarcc1, Npm3, No15, Lamr1, H1fx, Lmnb1, Spnr, Npm3, No1a1 Mki67ip, Ppan, Rnac, Grwd1, Srr, Pycs Pcbd, Mrps5, Lamr1, Mrp112, Rp144, Eif2b, Tomm40, Slc15a2, Slc4a7, Slc4a-4, Rangnrf, Kpnb3, Ipo4, M1p, Stk39, Rbp1, Reck, Areg, Ros1, Arhu, Frat2, Traf4, Myc, Frat2, Cldn2, Ghb3, Gja1, Krt1-18, Col15a1, Dsg2, Ect2, Lcn2, Kng, Hgfac, Adora2b, Spint1, Adam19, Hpn, or a combination thereof, can indicate an increased risk or be indicative of lung cancer.

In another embodiment, detecting a decreased level of Cdkn2d, Lats2, Hey1, Stat1, Bnip2, capn2, Anp32a, Madh6, Foxf1a, Tbx3, Tcf21, Gata3, Sox2, Crap, Trim30, Klf7, Sox17, Sox18, Meis1, Foxf2, Satb1, Anp32a, Bmp6, Tgfb1, Dpt, Acvr11, Eng, Zfhx1a, Igfbp6, Igfbp6, Igfbp4, Socs2, Nfkbia, Sox7, Ptpre, Ptpns1, Rassf5, Fkbp7, Sema3f, Vsn11, Reck, Capn2, Cdh5, Spock2, Thbd, Tie1 Icam2, Tek, Nes, Vwf, Xlkd1, Sparc11, Marcks, Tenc1, Pcdha6, Lama4, Lama3, Pcdha4, Vtn, Vcam1, Tna, Stab1, Pmp22, Ptprb, Ptprg, Slfn2, Ndr2, Ets1, Sipa1, Ndn, Meox2, Rbp1, Sema7a, Sema3c, Sema3e, Tag1n, Ab1im1, or a combination thereof can indicate an increased risk or be indicative of non-small cell lung cancer.

In another embodiment, the one or more biomarker for characterizing a cancer is PTGFRN, CD166, CD164, CD82, TGFBR1, MET, EFNB2, ITGA6, TDGF1, HBEGF, ABCC4, ABCD3, TDE2, ITGB1, TNFRSF21, CD81, CD9, KIAA1324, CEACAM6, FZD6, FZD7, BMPR1A, JAG1, ITGAV, NOTCH2, SOX4, HES1, HES6, ATOH1, CDH1, EPHB2, MYB, MYC, SOX9, PCGF1, PCGF4, PCGF5, ALDH1A1, STRAP, TCF4, VIM, CD44, or a combination thereof, such as described in US Patent Application Publication No. US20080064049, which is herein incorporated by reference in its entirety. In one embodiment, the cancer is characterized as tumorigenic or non-tumorigenic. In some embodiments, the cancer characterized is colon cancer or head and neck cancer.

In one embodiment, an elevated level of one or more of PTGFRN, CD166, CD164, CD82, TGFBR1, MET, EFNB2, ITGA6, TDGF1, HBEGF, ABCC4, ABCD3, TDE2, ITGB1, TNFRSF21, CD81, CD9, KIAA1324, CEACAM6, FZD6, FZD7, BMPR1A, JAG1, ITGAV, NOTCH2, SOX4, HES1, HES6, ATOH1, CDH1, EPHB2, MYB, MYC, SOX9, PCGF1, PCGF4, PCGF5, ALDH1A1, STRAP, or a combination thereof is indicative of a tumorigenic cancer. In another embodiment, a reduced level of one or both of TCF4 or VIM is indicative of a tumorigenic cancer. In some embodiments, the membrane vesicle comprises a biomarker such as CD44, epithelial specific antigen (ESA), or both, and is indicative of a tumorigenic cancer. In yet other embodiments, the membrane vesicle indicative of a tumorigenic cancer has an elevated level of CD49f activity, ALDH activity, or both.

The level of the biomarker (i.e., expression level) or activity level can be compared to, or relative to, a membrane vesicle derived from a non-tumorigenic tumor cell.

In another embodiment, one or more biomarkers for characterizing a cancer is an antigen comprising an epitope of a cellular surface protein, an antigen comprising an epitope of an aberrant protein glycosylation, or both, such as described in US Patent Application Publication No. US20090130125, which is herein incorporated by reference in its entirety. In one embodiment, the epitope is of a cellular adhesion protein, such as EpCAM, NCAM, Her-2/neu receptor or CEA. In another embodiment, the epitope is of a surface receptor, such as a receptor molecule selected from the group of the EGF receptor family, CD55 receptor, transferrin receptor and P-glycoprotein. In one embodiment, the antigen comprises an epitope of a carbohydrate selected from the group of Lewis antigens. The Lewis antigen can be a Lewis Y, Lewis B, sialyl-Tn, Globe H, or a combination thereof.

In another embodiment, one or more biomarkers for characterizing a cancer is EpCam or a polypeptide as described in US Patent Application Publication No. US20050084913, which is herein incorporated by reference in its entirety. The one or more biomarker can comprise a peptide sequence of SEQ ID NO: 4 therein, or a fragment thereof. In some embodiments, the biomarker has at least about 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% sequence identity with SEQ ID NO: 4 therein. In one embodiment, the biomarker comprises amino acid residues 81-265 of SEQ ID NO: 4 therein. In another embodiment, the biomarker comprises amino acid residues 24-265 of SEQ ID NO: 4 therein.

In another embodiment, one or more biomarkers for characterizing a cancer is CD3, CD4, CD8, CD14, CD19, CD56, mIgG1, CD2, CD5, CD7, CD9, CD10, CD11b, CD11c, CD13, CD15, CD16, CD20, CD21, CD22, CD23, CD24, CD25, CD33, CD34, CD36, CD37, CD38, CD41, CD42a, CD45, CD45RA, CD45RO, CD52, CD57, CD61, CD71, CD95, CD103, CD117, CD122, CD154, GPA, HLA-DR, KOR, FMC7, anti-hIg, mIgG2a, mIg2b, and mIgM, Anti-Ig, IgG2a, Kappa, Lambda, or a combination thereof, such as described in U.S. Pat. No. 7,560,226, which is herein incorporated by reference in its entirety. In one embodiment, the cancer is leukemia. In some embodiments, assess a membrane vesicle for the one or more biomarkers is used to distinguishing a leukemia of T cell, B cell, or myeloid lineage.

In another embodiment, one or more biomarkers for characterizing a cancer, such as breast cancer, is mammaglobin, PIP, B305D, B726, GABA, PDEF, CK19, lumican, selenoprotein P, connective tissue growth factor, EPCAM, E-cadherin, collagen, type IV, a-2. 6, or a combination thereof, such as described in PCT Publication No. WO2005118875, which is herein incorporated by reference in its entirety. Characterizing a breast cancer can comprise diagnosing the presence or predicting the course of breast cancer, or identifying a subject as at risk for metastasis.

In another embodiment, the one or more biomarker for characterizing an inflammatory condition or disease is Syntaxin1a, FCAR, SDR1, PTPN7, FABP5, CD9, or a combination thereof, such as described in US Patent Application Publication No. US20090226902, which is herein incorporated by reference in its entirety.

In one embodiment, characterizing an inflammatory condition comprises monitoring, screening, diagnosing, or predicting the development of the inflammatory disease. In one embodiment, the inflammatory condition is an auto-inflammatory disease or condition. In one embodiment, the inflammatory condition is an affective disorder, such as bipolar disease or depression. In yet another embodiment characterizing an inflammatory condition comprises determining an increased risk of developing an affective disorder.

In some embodiments, the one or more biomarker for characterizing a cardiovascular condition is CD34, CD9, CD29, CD34, CD44, CD45, CD49e, CD54, CD71, CD90, CD105, CD106, CD120a, CD124, CD166, Sca-1, SH2, SH3, HLA Class I, or a combination thereof, such as described in PCT Publication No. WO2006004910, which is herein incorporated by reference in its entirety.

In some embodiments, the one or more biomarker for characterizing Parkinson's Disease is ALDH1A1, ARPP-21, HSPA8, SKP1A, SLC18A2, SRPK2, TMEFF1, TRIM36, ADH5, PSMA3, PSMA2, PSMA5, PSMC4, HIP2, PACE4, COX6A1, PFKP, OXCT, GBE1, UQCRC2, LANCL1, TRIP15, PIK3CA, PLCL1, GNG5, GNAI1, VEGF, RHOB, NR4A2, SCL31A2, SCP2, PIGH, ARIH2, GMPR2, PP, IKBKAP, PRKACB, PTPRN2, BCAS2, TARS, PPP1R8, SEP15, TAF9, ZFP103, WRB, TMEM4, SMARCA3, FMR1, PDE6D, SGCE, AUH, SLC16A7, ATP6V1E1, UGTREL1, SEC22L1, CD9, CDH19, DUSP1, HSA6591, ACTR3, KIF2, TUBB2, ASPA, HELO1, C3orf4, CBR1, XPOT, LOC51142, NY-REN-45, SETO-2, EGLN1, EIF4EBP2, LGALS9, LOC56920, LRP6, MAN2B1, PARVA, PENK, SELPLG, SPHK1, SRRM2, ZSIG11, ITGB3BP, ITGAM, COL18A1, TM4SF9, LAMB2, HS3ST2, TSTA3, COL5A3, PALM, MYOM1, FLNB, HMBS, KRT2A, CSK, NUDC, HYPE, GAK, SIAT1, CSF1R, ICSBP1, CD22, ERCC1, DNAJB5, TRAF3, MMP9, EIF4G1, RPL36, SRPK1, CSNK1G2, RPS6KA1, JIK, LNK, INPP5D, TCOF1, NAPG, SLC19A1, ITSN1, LOC51035, PMVK, C21orf2, EFEMP2, TBL1X, APRT, SPUF, GLTSCR2, ADIR, PSCD4, CBFA2T1, CUGBP1, ING4, STAT6, ZNF239, TAL1, TAF11, MXD4, RDHL, LOC51157, LRP6, MBD3, C9orf7, or a combination thereof. The one or more biomarkers can be used for the detection, prognosis, monitoring, or theranosis of Parkinson's Disease, such as disclosed in PCT Publication No. WO2005067391, which is herein incorporated by reference in its entirety.

In some embodiments, the one or more biomarker for characterizing Diabetes Mellitus Type 1 is STX1A, MCP-3, CCL2, HSPAIA, HSPA1B, EMP1, BAZ1A, CD9, PTPN7, CDC42, FABP5, NAB2, SDR, or a combination thereof. The one or more biomarkers can be used for the detection, diagnosis, screening, or identification of Diabetes Mellitus Type 1, such as disclosed in PCT Publication No. WO200505451, which is herein incorporated by reference in its entirety.

In another embodiment, the one or more biomarker for characterizing an autoimmune condition is CD10, CD19, CD20, CD21, CD22, CD23, CD24, CD37, CD40, CD53, CD72, CD73, CD74, CDw75, CDw76, CD77, CDw78, CD79a, CD79b, CD80, CD81, CD82, CD83, CDw84, CD85, CD86, or a combination thereof, such as described in US Patent Application Publication No. US20080213280, which is herein incorporated by reference in its entirety.

In one embodiment, assessing a membrane vesicle for CD10, CD19, CD20, CD21, CD22, CD23, CD24, CD37, CD40, CD53, CD72, CD73, CD74, CDw75, CDw76, CD77, CDw78, CD79a, CD79b, CD80, CD81, CD82, CD83, CDw84, CD85, CD86, or a combination thereof can be used to select a treatment, such as an antibody that binds CD20, methotrexate (MTX), a corticosteroid regimen, or a combination thereof. The antibody can comprise rituximab, such as disclosed in US Patent Application Publication No. US20080213280. In one embodiment, the subject is treated with rituximab and concomitant methotrexate (MTX). In another embodiment, the subject is further treated with a corticosteroid regimen. In some embodiments, the corticosteroid regimen comprises of methylprednisolone, prednisone, or a combination thereof.

In another embodiment, assessing a membrane vesicle for CD10, CD19, CD20, CD21, CD22, CD23, CD24, CD37, CD40, CD53, CD72, CD73, CD74, CDw75, CDw76, CD77, CDw78, CD79a, CD79b, CD80, CD81, CD82, CD83, CDw84, CD85, CD86, or a combination thereof can be used to assess rheumatoid arthritis in a subject, such as assessing whether a subject experiences an inadequate response to a TNFα-inhibitor. In another embodiment, assessing a membrane vesicle for CD10, CD19, CD20, CD21, CD22, CD23, CD24, CD37, CD40, CD53, CD72, CD73, CD74, CDw75, CDw76, CD77, CDw78, CD79a, CD79b, CD80, CD81, CD82, CD83, CDw84, CD85, CD86, or a combination thereof can be used to determine if a subject will have a negative side effect, such as an infection, heart failure, demyelination, or a combination thereof, as a result of treatment for an autoimmune condition.

The autoimmune disease or condition can be, but not limited to, arthritis, rheumatoid arthritis, juvenile rheumatoid arthritis, osteoarthritis, psoriatic arthritis, psoriasis, dermatitis, polymyositis/dermatomyositis, toxic epidermal necrolysis, systemic scleroderma and sclerosis, responses associated with inflammatory bowel disease, Crohn's disease, ulcerative colitis, respiratory distress syndrome, adult respiratory distress syndrome (ARDS), meningitis, encephalitis, uveitis, colitis, glomerulonephritis, allergic conditions, eczema, asthma, conditions involving infiltration of T cells and chronic inflammatory responses, atherosclerosis, autoimmune myocarditis, leukocyte adhesion deficiency, systemic lupus erythematosus (SLE), juvenile onset diabetes, multiple sclerosis, allergic encephalomyelitis, immune responses associated with acute and delayed hypersensitivity mediated by cytokines and T-lymphocytes, tuberculosis, sarcoidosis, granulomatosis including Wegener's granulomatosis, agranulocytosis, vasculitis (including ANCA), aplastic anemia, Diamond Blackfan anemia, immune hemolytic anemia including autoimmune hemolytic anemia (AIHA), pernicious anemia, pure red cell aplasia (PRCA), Factor VIII deficiency, hemophilia A, autoimmune neutropenia, pancytopenia, leukopenia, diseases involving leukocyte diapedesis, central nervous system (CNS) inflammatory disorders, multiple organ injury syndrome, mysathenia gravis, antigen-antibody complex mediated diseases, anti-glomerular basement membrane disease, anti-phospholipid antibody syndrome, allergic neuritis, Bechet disease, Castleman's syndrome, Goodpasture's syndrome, Lambert-Eaton Myasthenic Syndrome, Reynaud's syndrome, Sjorgen's syndrome, Stevens-Johnson syndrome, solid organ transplant rejection, graft versus host disease (GVHD), pemphigoid bullous, pemphigus, autoimmune polyendocrinopathies, Reiter's disease, stiff-man syndrome, giant cell arteritis, immune complex nephritis, IgA nephropathy, IgM polyneuropathies or IgM mediated neuropathy, idiopathic thrombocytopenic purpura (ITP), thrombotic throbocytopenic purpura (TTP), autoimmune thrombocytopenia, autoimmune disease of the testis and ovary including autoimmune orchitis and oophoritis, primary hypothyroidism; autoimmune endocrine diseases including autoimmune thyroiditis, chronic thyroiditis (Hashimoto's Thyroiditis), subacute thyroiditis, idiopathic hypothyroidism, Addison's disease, Grave's disease, autoimmune polyglandular syndromes (or polyglandular endocrinopathy syndromes), Type I diabetes also referred to as insulin-dependent diabetes mellitus (IDDM) and Sheehan's syndrome; autoimmune hepatitis, lymphoid interstitial pneumonitis (HIV), bronchiolitis obliterans (non-transplant) vs NSIP, Guillain-Barre' Syndrome, large vessel vasculitis (including polymyalgia rheumatica and giant cell (Takayasu's) arteritis), medium vessel vasculitis (including Kawasaki's disease and polyarteritis nodosa), ankylosing spondylitis, Berger's disease (IgA nephropathy), rapidly progressive glomerulonephritis, primary biliary cirrhosis, Celiac sprue (gluten enteropathy), cryoglobulinemia, amyotrophic lateral sclerosis (ALS), or coronary artery disease.

As described, biomarkers useful to carry out the methods of the invention include miRNAs that interact with genes (including gene products) of interest. The miRNA that interacts with PFKFB3 can be miR-513a-3p, miR-128, miR-488, miR-539, miR-658, miR-524-5p, miR-1258, miR-150, miR-216b, miR-377, miR-135a, miR-26a, miR-548a-5p, miR-26b, miR-520d-5p, miR-224, miR-1297, miR-1197, miR-182, miR-452, miR-509-3-5p, miR-548m, miR-625, miR-509-5p, miR-1266, miR-135b, miR-190b, miR-496, miR-616, miR-621, miR-650, miR-105, miR-19a, miR-346, miR-620, miR-637, miR-651, miR-1283, miR-590-3p, miR-942, miR-1185, miR-577, miR-602, miR-1305, miR-220c, miR-1270, miR-1282, miR-432, miR-491-5p, miR-548n, miR-765, miR-768-3p or miR-924, and can be used as a biomarker.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with PFKFB3. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with PFKFB3. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with PFKFB3. Furthermore, the one or more miRNA that interacts with PFKFB3 can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with PFKFB3 of one or more vesicles of a biological sample.

The miRNA that interacts with RHAMM can be miR-936, miR-656, miR-105, miR-361-5p, miR-194, miR-374a, miR-590-3p, miR-186, miR-769-5p, miR-892a, miR-380, miR-875-3p, miR-208a, miR-208b, miR-586, miR-125a-3p, miR-630, miR-374b, miR-411, miR-629, miR-1286, miR-1185, miR-16, miR-200b, miR-671-5p, miR-95, miR-421, miR-496, miR-633, miR-1243, miR-127-5p, miR-143, miR-15b, miR-200c, miR-24 or miR-34c-3p. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with RHAMM. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with RHAMM. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with RHAMM. Furthermore, the one or more miRNA that interacts with RHAMM can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with RHAMM of one or more vesicles of a biological sample.

The miRNA that interacts with CENPF can be miR-30c, miR-30b, miR-190, miR-508-3p, miR-384, miR-512-5p, miR-548p, miR-297, miR-520f, miR-376a, miR-1184, miR-577, miR-708, miR-205, miR-376b, miR-520g, miR-520h, miR-519d, miR-596, miR-768-3p, miR-340, miR-620, miR-539, miR-567, miR-671-5p, miR-1183, miR-129-3p, miR-636, miR-106a, miR-1301, miR-17, miR-20a, miR-570, miR-656, miR-1263, miR-1324, miR-142-5p, miR-28-5p, miR-302b, miR-452, miR-520d-3p, miR-548o, miR-892b, miR-302d, miR-875-3p, miR-106b, miR-1266, miR-1323, miR-20b, miR-221, miR-520e, miR-664, miR-920, miR-922, miR-93, miR-1228, miR-1271, miR-30e, miR-483-3p, miR-509-3-5p, miR-515-3p, miR-519e, miR-520b, miR-520c-3p or miR-582-3p. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides a vesicle comprising one or more one or more miRNA that interacts with CENPF. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with CENPF. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with CENPF. Furthermore, the one or more miRNA that interacts with CENPF can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with CENPF of one or more vesicles of a biological sample.

The miRNA that interacts with NCAPG can be miR-876-5p, miR-1260, miR-1246, miR-548c-3p, miR-1224-3p, miR-619, miR-605, miR-490-5p, miR-186, miR-448, miR-129-5p, miR-188-3p, miR-516b, miR-342-3p, miR-1270, miR-548k, miR-654-3p, miR-1290, miR-656, miR-34b, miR-520g, miR-1231, miR-1289, miR-1229, miR-23a, miR-23b, miR-616 or miR-620. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with NCAPG. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with NCAPG. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with NCAPG. Furthermore, the one or more miRNA that interacts with NCAPG can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with NCAPG of one or more vesicles of a biological sample.

The miRNA that interacts with Androgen Receptor can be miR-124a, miR-130a, miR-130b, miR-143, miR-149, miR-194, miR-29b, miR-29c, miR-301, miR-30a-5p, miR-30d, miR-30e-5p, miR-337, miR-342, miR-368, miR-488, miR-493-5p, miR-506, miR-512-5p, miR-644, miR-768-5p or miR-801. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with AR. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with AR. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with AR. Furthermore, the one or more miRNA that interacts with AR can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with AR of one or more vesicles of a biological sample.

The miRNA that interacts with EGFR can be miR-105, miR-128a, miR-128b, miR-140, miR-141, miR-146a, miR-146b, miR-27a, miR-27b, miR-302a, miR-302d, miR-370, miR-548c, miR-574, miR-587 or miR-7. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with EGFR. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with EGFR. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with EGFR. Furthermore, the one or more miRNA that interacts with EGFR can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with AR of one or more vesicles of a biological sample.

The miRNA that interacts with HSP90 can be miR-1, miR-513a-3p, miR-548d-3p, miR-642, miR-206, miR-450b-3p, miR-152, miR-148a, miR-148b, miR-188-3p, miR-23a, miR-23b, miR-578, miR-653, miR-1206, miR-192, miR-215, miR-181b, miR-181d, miR-223, miR-613, miR-769-3p, miR-99a, miR-100, miR-454, miR-548n, miR-640, miR-99b, miR-150, miR-181a, miR-181c, miR-522, miR-624, miR-130a, miR-130b, miR-146, miR-148a, miR-148b, miR-152, miR-181a, miR-181b, miR-181c, miR-204, miR-206, miR-211, miR-212, miR-215, miR-223, miR-23a, miR-23b, miR-301, miR-31, miR-325, miR-363, miR-566, miR-9 or miR-99b. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with HSP90. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with HSP90. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with HSP90. Furthermore, the one or more miRNA that interacts with HSP90 can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with HSP90 of one or more vesicles of a biological sample.

The miRNA that interacts with SPARC can be miR-768-5p, miR-203, miR-196a, miR-569, miR-187, miR-641, miR-1275, miR-432, miR-622, miR-296-3p, miR-646, miR-196b, miR-499-5p, miR-590-5p, miR-495, miR-625, miR-1244, miR-512-5p, miR-1206, miR-1303, miR-186, miR-302d, miR-494, miR-562, miR-573, miR-10a, miR-203, miR-204, miR-211, miR-29, miR-29b, miR-29c, miR-339, miR-433, miR-452, miR-515-5p, miR-517a, miR-517b, miR-517c, miR-592 or miR-96. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with SPARC. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with SPARC. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with SPARC. Furthermore, the one or more miRNA that interacts with SPARC can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with SPARC of one or more vesicles of a biological sample.

The miRNA that interacts with DNMT3B can be miR-618, miR-1253, miR-765, miR-561, miR-330-5p, miR-326, miR-188, miR-203, miR-221, miR-222, miR-26a, miR-26b, miR-29a, miR-29b, miR-29c, miR-370, miR-379, miR-429, miR-519e, miR-598, miR-618 or miR-635. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with DNMT3B. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with DNMT3B. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with DNMT3B. Furthermore, the one or more miRNA that interacts with DNMT3B can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with DNMT3B of one or more vesicles of a biological sample.

The miRNA that interacts with GART can be miR-101, miR-141, miR-144, miR-182, miR-189, miR-199a, miR-199b, miR-200a, miR-200b, miR-202, miR-203, miR-223, miR-329, miR-383, miR-429, miR-433, miR-485-5p, miR-493-5p, miR-499, miR-519a, miR-519b, miR-519c, miR-569, miR-591, miR-607, miR-627, miR-635, miR-636 or miR-659. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with GART. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with GART. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with GART. Furthermore, the one or more miRNA that interacts with GART can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with GART of one or more vesicles of a biological sample.

The miRNA that interacts with MGMT can be miR-122a, miR-142-3p, miR-17-3p, miR-181a, miR-181b, miR-181c, miR-181d, miR-199b, miR-200a, miR-217, miR-302b, miR-32, miR-324-3p, miR-34a, miR-371, miR-425-5p, miR-496, miR-514, miR-515-3p, miR-516-3p, miR-574, miR-597, miR-603, miR-653, miR-655, miR-92, miR-92b or miR-99a. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with MGMT. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with MGMT. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with MGMT. Furthermore, the one or more miRNA that interacts with MGMT can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with MGMT of one or more vesicles of a biological sample.

The miRNA that interacts with SSTR3 can be miR-125a, miR-125b, miR-133a, miR-133b, miR-136, miR-150, miR-21, miR-380-5p, miR-504, miR-550, miR-671, miR-766 or miR-767-3p. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides an isolated vesicle comprising one or more one or more miRNA that interacts with SSTR3. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with SSTR3. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with SSTR3. Furthermore, the one or more miRNA that interacts with SSTR3 can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with SSTR3 of one or more vesicles of a biological sample.

The miRNA that interacts with TOP2B can be miR-548f, miR-548a-3p, miR-548g, miR-513a-3p, miR-548c-3p, miR-101, miR-653, miR-548d-3p, miR-575, miR-297, miR-576-3p, miR-548b-3p, miR-624, miR-548n, miR-758, miR-1253, miR-1324, miR-23b, miR-320a, miR-320b, miR-1183, miR-1244, miR-23a, miR-451, miR-568, miR-1276, miR-548e, miR-590-3p, miR-1, miR-101, miR-126, miR-129, miR-136, miR-140, miR-141, miR-144, miR-147, miR-149, miR-18, miR-181b, miR-181c, miR-182, miR-184, miR-186, miR-189, miR-191, miR-19a, miR-19b, miR-200a, miR-206, miR-210, miR-218, miR-223, miR-23a, miR-23b, miR-24, miR-27a, miR-302, miR-30a, miR-31, miR-320, miR-323, miR-362, miR-374, miR-383, miR-409-3p, miR-451, miR-489, miR-493-3p, miR-514, miR-542-3p, miR-544, miR-548a, miR-548b, miR-548c, miR-548d, miR-559, miR-568, miR-575, miR-579, miR-585, miR-591, miR-598, miR-613, miR-649, miR-651, miR-758, miR-768-3p or miR-9. These miRNAs can be used as biomarkers according to the methods of the invention.

The invention also provides a vesicle comprising one or more one or more miRNA that interacts with TOP2B. The invention further provides a composition comprising the isolated vesicle. Accordingly, in some embodiments, the composition comprises a population of vesicles comprising one or more biomarkers consisting of miRNA that interacts with TOP2B. The composition can comprise a substantially enriched population of vesicles, wherein the population of vesicles is substantially homogeneous for vesicles comprising one or more miRNA that interacts with TOP2B. Furthermore, the one or more miRNA that interacts with TOP2B can also be detected by one or more systems disclosed herein. For example, a detection system can comprise one or more probes to detect one or more one or more miRNA that interacts with TOP2B of one or more vesicles of a biological sample.

Other MicroRNA Biomarkers

Other microRNAs that can be detected or assessed in a vesicle and used to characterize a phenotype include, but are not limited to, hsa-let-7a, hsa-let-7b, hsa-let-7c, hsa-let-7d, hsa-let-7e, hsa-let-7f, hsa-miR-15a, hsa-miR-16, hsa-miR-17-5p, hsa-miR-1′7-3p, hsa-miR-18a, hsa-miR-19a, hsa-miR-19b, hsa-miR-20a, hsa-miR-21, hsa-miR-22, hsa-miR-23a, hsa-miR-189, hsa-miR-24, hsa-miR-25, hsa-miR-26a, hsa-miR-26b, hsa-miR-27a, hsa-miR-28, hsa-miR-29a, hsa-miR-30a-5p, hsa-miR-30a-3p, hsa-miR-31, hsa-miR-32, hsa-miR-33, hsa-miR-92, hsa-miR-93, hsa-miR-95, hsa-miR-96, hsa-miR-98, hsa-miR-99a, hsa-miR-100, hsa-miR-101, hsa-miR-29b, hsa-miR-103, hsa-miR-105, hsa-miR-106a, hsa-miR-107, hsa-miR-192, hsa-miR-196a, hsa-miR-197, hsa-miR-198, hsa-miR-199a, hsa-miR-199a*, hsa-miR-208, hsa-miR-129, hsa-miR-148a, hsa-miR-30c, hsa-miR-30d, hsa-miR-139, hsa-miR-147, hsa-miR-7, hsa-miR-10a, hsa-miR-10b, hsa-miR-34a, hsa-miR-181a, hsa-miR-181b, hsa-miR-181c, hsa-miR-182, hsa-miR-182*, hsa-miR-183, hsa-miR-187, hsa-miR-199b, hsa-miR-203, hsa-miR-204, hsa-miR-205, hsa-miR-210, hsa-miR-211, hsa-miR-212, hsa-miR-181a*, hsa-miR-214, hsa-miR-215, hsa-miR-216, hsa-miR-217, hsa-miR-218, hsa-miR-219, hsa-miR-220, hsa-miR-221, hsa-miR-222, hsa-miR-223, hsa-miR-224, hsa-miR-200b, hsa-let-7g, hsa-let-71, hsa-miR-1, hsa-miR-15b, hsa-miR-23b, hsa-miR-27b, hsa-miR-30b, hsa-miR-122a, hsa-miR-124a, hsa-miR-125b, hsa-miR-128a, hsa-miR-130a, hsa-miR-132, hsa-miR-133a, hsa-miR-135a, hsa-miR-137, hsa-miR-138, hsa-miR-140, hsa-miR-141, hsa-miR-142-5p, hsa-miR-142-3p, hsa-miR-143, hsa-miR-144, hsa-miR-145, hsa-miR-152, hsa-miR-153, hsa-miR-191, hsa-miR-9, hsa-miR-9*, hsa-miR-125a, hsa-miR-126*, hsa-miR-126, hsa-miR-127, hsa-miR-134, hsa-miR-136, hsa-miR-146a, hsa-miR-149, hsa-miR-150, hsa-miR-154, hsa-miR-154*, hsa-miR-184, hsa-miR-185, hsa-miR-186, hsa-miR-188, hsa-miR-190, hsa-miR-193a, hsa-miR-194, hsa-miR-195, hsa-miR-206, hsa-miR-320, hsa-miR-200c, hsa-miR-155, hsa-miR-128b, hsa-miR-106b, hsa-miR-29c, hsa-miR-200a, hsa-miR-302a*, hsa-miR-302a, hsa-miR-34b, hsa-miR-34c, hsa-miR-299-3p, hsa-miR-301, hsa-miR-99b, hsa-miR-296, hsa-miR-130b, hsa-miR-30e-5p, hsa-miR-30e-3p, hsa-miR-361, hsa-miR-362, hsa-miR-363, hsa-miR-365, hsa-mir-302b*, hsa-miR-302b, hsa-miR-302c*, hsa-miR-302c, hsa-miR-302d, hsa-miR-367, hsa-miR-368, hsa-miR-369-3p, hsa-miR-370, hsa-miR-371, hsa-miR-372, hsa-miR-373*, hsa-miR-373, hsa-miR-374, hsa-miR-375, hsa-miR-376a, hsa-miR-377, hsa-miR-378, hsa-miR-422b, hsa-miR-379, hsa-miR-380-5p, hsa-miR-380-3p, hsa-miR-381, hsa-miR-382, hsa-miR-383, hsa-miR-340, hsa-miR-330, hsa-miR-328, hsa-miR-342, hsa-miR-337, hsa-miR-323, hsa-miR-326, hsa-miR-151, hsa-miR-135b, hsa-miR-148b, hsa-miR-331, hsa-miR-324-5p, hsa-miR-324-3p, hsa-miR-338, hsa-miR-339, hsa-miR-335, hsa-miR-133b, hsa-miR-325, hsa-miR-345, hsa-miR-346, ebv-miR-BHRF1-1, ebv-miR-BHRF1-2*, ebv-miR-BHRF1-2, ebv-miR-BHRF1-3, ebv-miR-BART1-5p, ebv-miR-BART2, hsa-miR-384, hsa-miR-196b, hsa-miR-422a, hsa-miR-423, hsa-miR-424, hsa-miR-425-3p, hsa-miR-18b, hsa-miR-20b, hsa-miR-448, hsa-miR-429, hsa-miR-449, hsa-miR-450, hcmv-miR-UL22A, hcmv-miR-UL22A*, hcmv-miR-UL36, hcmv-miR-UL112, hcmv-miR-UL148D, hcmv-miR-US5-1, hcmv-miR-US5-2, hcmv-miR-US25-1, hcmv-miR-US25-2-5p, hcmv-miR-US25-2-3p, hcmv-miR-US33, hsa-miR-191*, hsa-miR-200a*, hsa-miR-369-5p, hsa-miR-431, hsa-miR-433, hsa-miR-329, hsa-miR-453, hsa-miR-451, hsa-miR-452, hsa-miR-452*, hsa-miR-409-5p, hsa-miR-409-3p, hsa-miR-412, hsa-miR-410, hsa-miR-376b, hsa-miR-483, hsa-miR-484, hsa-miR-485-5p, hsa-miR-485-3p, hsa-miR-486, hsa-miR-487a, kshv-miR-K12-10a, kshv-miR-K12-10b, kshv-miR-K12-11, kshv-miR-K12-1, kshv-miR-K12-2, kshv-miR-K12-9*, kshv-miR-K12-9, kshv-miR-K12-8, kshv-miR-K12-7, kshv-miR-K12-6-5p, kshv-miR-K12-6-3p, kshv-miR-K12-5, kshv-miR-K12-4-5p, kshv-miR-K12-4-3p, kshv-miR-K12-3, kshv-miR-K12-3*, hsa-miR-488, hsa-miR-489, hsa-miR-490, hsa-miR-491, hsa-miR-511, hsa-miR-146b, hsa-miR-202*, hsa-miR-202, hsa-miR-492, hsa-miR-493-5p, hsa-miR-432, hsa-miR-432*, hsa-miR-494, hsa-miR-495, hsa-miR-496, hsa-miR-193b, hsa-miR-497, hsa-miR-181d, hsa-miR-512-5p, hsa-miR-512-3p, hsa-miR-498, hsa-miR-520e, hsa-miR-515-5p, hsa-miR-515-3p, hsa-miR-519e*, hsa-miR-519e, hsa-miR-520f, hsa-miR-526c, hsa-miR-519c, hsa-miR-520a*, hsa-miR-520a, hsa-miR-526b, hsa-miR-526b*, hsa-miR-519b, hsa-miR-525, hsa-miR-525*, hsa-miR-523, hsa-miR-518% hsa-miR-518f, hsa-miR-520b, hsa-miR-518b, hsa-miR-526a, hsa-miR-520c, hsa-miR-518c*, hsa-miR-518c, hsa-miR-524*, hsa-miR-524, hsa-miR-517*, hsa-miR-517a, hsa-miR-519d, hsa-miR-521, hsa-miR-520d*, hsa-miR-520d, hsa-miR-517b, hsa-miR-520g, hsa-miR-516-5p, hsa-miR-516-3p, hsa-miR-518e, hsa-miR-527, hsa-miR-518a, hsa-miR-518d, hsa-miR-517c, hsa-miR-520h, hsa-miR-522, hsa-miR-519a, hsa-miR-499, hsa-miR-500, hsa-miR-501, hsa-miR-502, hsa-miR-503, hsa-miR-504, hsa-miR-505, hsa-miR-513, hsa-miR-506, hsa-miR-507, hsa-miR-508, hsa-miR-509, hsa-miR-510, hsa-miR-514, hsa-miR-532, hsa-miR-299-5p, hsa-miR-18a*, hsa-miR-455, hsa-miR-493-3p, hsa-miR-539, hsa-miR-544, hsa-miR-545, hsa-miR-487b, hsa-miR-551a, hsa-miR-552, hsa-miR-553, hsa-miR-554, hsa-miR-92b, hsa-miR-555, hsa-miR-556, hsa-miR-557, hsa-miR-558, hsa-miR-559, hsa-miR-560, hsa-miR-561, hsa-miR-562, hsa-miR-563, hsa-miR-564, hsa-miR-565, hsa-miR-566, hsa-miR-567, hsa-miR-568, hsa-miR-551b, hsa-miR-569, hsa-miR-570, hsa-miR-571, hsa-miR-572, hsa-miR-573, hsa-miR-574, hsa-miR-575, hsa-miR-576, hsa-miR-577, hsa-miR-578, hsa-miR-579, hsa-miR-580, hsa-miR-581, hsa-miR-582, hsa-miR-583, hsa-miR-584, hsa-miR-585, hsa-miR-548a, hsa-miR-586, hsa-miR-587, hsa-miR-548b, hsa-miR-588, hsa-miR-589, hsa-miR-550, hsa-miR-590, hsa-miR-591, hsa-miR-592, hsa-miR-593, hsa-miR-595, hsa-miR-596, hsa-miR-597, hsa-miR-598, hsa-miR-599, hsa-miR-600, hsa-miR-601, hsa-miR-602, hsa-miR-603, hsa-miR-604, hsa-miR-605, hsa-miR-606, hsa-miR-607, hsa-miR-608, hsa-miR-609, hsa-miR-610, hsa-miR-611, hsa-miR-612, hsa-miR-613, hsa-miR-614, hsa-miR-615, hsa-miR-616, hsa-miR-548c, hsa-miR-617, hsa-miR-618, hsa-miR-619, hsa-miR-620, hsa-miR-621, hsa-miR-622, hsa-miR-623, hsa-miR-624, hsa-miR-625, hsa-miR-626, hsa-miR-627, hsa-miR-628, hsa-miR-629, hsa-miR-630, hsa-miR-631, hsa-miR-33b, hsa-miR-632, hsa-miR-633, hsa-miR-634, hsa-miR-635, hsa-miR-636, hsa-miR-637, hsa-miR-638, hsa-miR-639, hsa-miR-640, hsa-miR-641, hsa-miR-642, hsa-miR-643, hsa-miR-644, hsa-miR-645, hsa-miR-646, hsa-miR-647, hsa-miR-648, hsa-miR-649, hsa-miR-650, hsa-miR-651, hsa-miR-652, hsa-miR-548d, hsa-miR-661, hsa-miR-662, hsa-miR-663, hsa-miR-449b, hsa-miR-653, hsa-miR-411, hsa-miR-654, hsa-miR-655, hsa-miR-656, hsa-miR-549, hsa-miR-657, hsa-miR-658, hsa-miR-659, hsa-miR-660, hsa-miR-421, hsa-miR-542-5p, hcmv-miR-US4, hcmv-miR-UL70-5p, hcmv-miR-UL70-3p, hsa-miR-363*, hsa-miR-376a*, hsa-miR-542-3p, ebv-miR-BART1-3p, hsa-miR-425-5p, ebv-miR-BART3-5p, ebv-miR-BART3-3p, ebv-miR-BART4, ebv-miR-BART5, ebv-miR-BART6-5p, ebv-miR-BART6-3p, ebv-miR-BART7, ebv-miR-BART8-5p, ebv-miR-BART8-3p, ebv-miR-BART9, ebv-miR-BART10, ebv-miR-BART11-5p, ebv-miR-BART11-3p, ebv-miR-BART12, ebv-miR-BART13, ebv-miR-BART14-5p, ebv-miR-BART14-3p, kshv-miR-K12-12, ebv-miR-BART15, ebv-miR-BART16, ebv-miR-BART17-5p, ebv-miR-BART17-3p, ebv-miR-BART18, ebv-miR-BART19, ebv-miR-BART20-5p, ebv-miR-BART20-3p, hsv1-miR-H1, hsa-miR-758, hsa-miR-671, hsa-miR-668, hsa-miR-767-5p, hsa-miR-767-3p, hsa-miR-454-5p, hsa-miR-454-3p, hsa-miR-769-5p, hsa-miR-769-3p, hsa-miR-766, hsa-miR-765, hsa-miR-768-5p, hsa-miR-768-3p, hsa-miR-770-5p, hsa-miR-802, hsa-miR-801, and hsa-miR-675.

It has been observed that miR-128A, miR-129 and miR-128B are enriched in brain; miR-194, miR-148 and miR-192 are enriched in liver; miR-96, miR-150, miR-205, miR-182 and miR-183 are enriched in the thymus; miR-204, miR-10B, miR-154 and miR-134 are enriched in testes; and miR-122, miR-210, miR-221, miR-141, miR-23A, miR-200C and miR-136 are enriched in the placenta. The biosignature comprising one or more of the aforementioned miRs can be used to detect vesicles of interest, e.g., vesicles useful in distinguishing positive and negative lymph nodes from a subject with a cancer, e.g., cervical, brain, liver, thymus, testical, colon or breast cancer.

In another embodiment, a biosignature can comprise one or more of the following miRs: miR-125b-1, miR125b-2, miR-145, miR-21, miR-155, miR-10b, miR-009-1 (miR131-1), miR-34 (miR-170), miR-102 (miR-29b), miR-123 (miR-126), miR-140-as, miR-125a, miR-125b-1, miR-125b-2, miR-194, miR-204, miR-213, let-7a-2, let-7a-3, let-7d (let-7d-v1), let-7f-2, let-71 (let-7d-v2), miR-101-1, miR-122a, miR-128b, miR-136, miR-143, miR-149, miR-191, miR-196-1, miR-196-2, miR-202, miR-203, miR-206, and miR-210, which can be used to characterize breast cancer.

In another embodiment, miR-375 expression is detected in a vesicle and used to characterize pancreatic insular or acinar tumors.

In yet another embodiment, one or more of the following miRs can be detected in a vesicle: miR-103-2, miR-107, miR-103-1, miR-342, miR-100, miR-24-2, miR-23a, miR-125a, miR-26a-1, miR-24-1, miR-191, miR-15a, miR-368, miR-26b, miR-125b-2, miR-125b-1, miR-26a-2, miR-335, miR-126. miR-1-2, miR-21, miR-25, miR-92-2, miR-130a, miR-93, miR-16-1, miR-145, miR-17, miR-99b, miR-181b-1, miR-146, miR-181b-2, miR-16-2, miR-99a, miR-197, miR-10a, miR-224, miR-92-1, miR-27a, miR-221, miR-320, miR-7-1, miR-29b-2, miR-150, miR-30d, miR-29a, miR-23b, miR-135a-2, miR-223, miR-3p21-v, miR-128b, miR-30b, miR-29b-1, miR-106b, miR-132, miR-214, miR-7-3, miR-29c, miR-367, miR-30c-2, miR-27b, miR-140, miR-10b, miR-20, miR-129-1, miR-340, miR-30a, miR-30c-1, miR-106a, miR-32, miR-95, miR-222, miR-30e, miR-129-2, miR-345, miR-143, miR-182, miR-1-1, miR-133a-1, miR-200c, miR-194-1, miR-210, miR-181c, miR-192, miR-220, miR-213, miR-323, and miR-375, wherein high expression or overexpression of the one or more miRs can be used to characterize pancreatic cancer.

Expression of one or more of the following miRs: miR-101, miR-126, miR-99a, miR-99-prec, miR-106, miR-339, miR-99b, miR-149, miR-33, miR-135 and miR-20 can be detected in a vesicle and used to characterize megakaryocytopoiesis.

Cell proliferation has been correlated with the expression of miR-31, miR-92, miR-99a, miR-100, miR-125a, miR-129, miR-130a, miR-150, miR-187, miR-190, miR-191, miR-193, miR 204, miR-210, miR-21 1, miR-212, miR-213, miR-215, miR-216, miR-217, miR 218, miR-224, miR-292, miR-294, miR-320, miR-324, miR-325, miR-326, miR-330, miR-331, miR-338, miR-341, miR-369, miR-370, et-7a, Let-7b, Let-7c, Let-7d, Let-7g, miR-7, miR-9, miR-10a, miR-10b, miR-15a, miR-18, miR-19a, miR-17-3p, miR-20, miR-23b, miR-25, miR-26a, miR-26a, miR-30e-5p, miR-31, miR-32, miR-92, miR-93, miR-100, miR-125a, miR-125b, miR-126, miR-127, miR-128, miR-129, miR-130a, miR-135, miR-138, miR-139, miR-140, miR-141, miR-143, miR-145, miR-146, miR-150, miR-154, miR-155, miR-181a, miR-182, miR-186, miR-187, miR-188, miR-190, miR-191, miR-193, miR-194, miR-196, miR-197, miR-198, miR-199, miR-201, miR-204, miR-216, miR-218, miR-223, miR-293, miR-291-3p, miR-294, miR-295, miR-322, miR-333, miR-335, miR-338, miR-341, miR-350, miR-369, miR-373, miR-410, and miR-412. Detection one or more of the above miRs can be used to characterize a proliferative disorder, such as a cancer.

Other examples of miRs that can be detected in a vesicle and used to characterize cancer are disclosed in U.S. Pat. No. 7,642,348, describing identification of 3,765 unique nucleic acid sequences correlated with prostate cancer), and U.S. Pat. No. 7,592,441, which describes microRNAs related to liver cancer.

Other microRNAs that are expressed commonly in solid cancer, such as colon cancer, lung cancer, breast cancer, stomach cancer, prostate cancer, and pancreatic cancer, can also be detected in a vesicle and used to characterize a cancer. For example, one or more of the following miRs: miR-21, miR-17-5p, miR-191, miR-29b-2, miR-223, miR-128b, miR-199a-1, miR-24-1, miR-24-2, miR-146, miR-155, miR-181b-1, miR-20a, miR-107, miR-32, miR-92-2, miR-214, miR-30c, miR-25, miR-221, and miR-106a, can be detected in a vesicle and used to characterize a solid cancer.

Other examples of microRNAs that can be detected in a vesicle are disclosed in PCT Publication Nos. WO2006126040, WO2006033020, WO2005116250, and WO2005111211, US Publications Nos. US20070042982 and US20080318210; and EP Publication Nos. EP1784501A2 and EP1751311A2, each of which is incorporated by reference.

Biomarker Detection

A biosignature can be detected qualitatively or quantitatively by detecting a presence, level or concentration of a circulating biomarker, e.g., vesicle or other biomarkers, as disclosed herein. These biosignature components can be detected using a number of techniques known to those of skill in the art. For example, a biomarker can be detected by microarray analysis, polymerase chain reaction (PCR) (including PCR-based methods such as real time polymerase chain reaction (RT-PCR), quantitative real time polymerase chain reaction (Q-PCR/qPCR) and the like), hybridization with allele-specific probes, enzymatic mutation detection, ligation chain reaction (LCR), oligonucleotide ligation assay (OLA), flow-cytometric heteroduplex analysis, chemical cleavage of mismatches, mass spectrometry, nucleic acid sequencing, single strand conformation polymorphism (SSCP), denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), restriction fragment polymorphisms, serial analysis of gene expression (SAGE), or combinations thereof. A biomarker, such as a nucleic acid, can be amplified prior to detection. A biomarker can also be detected by immunoassay, immunoblot, immunoprecipitation, enzyme-linked immunosorbent assay (ELISA; EIA), radioimmunoassay (RIA), flow cytometry, or electron microscopy (EM).

Biosignatures can be detected using capture agents and detection agents, as described herein. A capture agent can comprise an antibody, aptamer or other entity which recognizes a biomarker and can be used for capturing the biomarker. Biomarkers that can be captured include circulating biomarkers, e.g., a protein, nucleic acid, lipid or biological complex in solution in a bodily fluid. Similarly, the capture agent can be used for capturing a vesicle. A detection agent can comprise an antibody or other entity which recognizes a biomarker and can be used for detecting the biomarker vesicle, or which recognizes a vesicle and is useful for detecting a vesicle. In some embodiments, the detection agent is labeled and the label is detected, thereby detecting the biomarker or vesicle. The detection agent can be a binding agent, e.g., an antibody or aptamer. In other embodiments, the detection agent comprises a small molecule such as a membrane protein labeling agent. See, e.g., the membrane protein labeling agents disclosed in Alroy et al., US. Patent Publication US 2005/0158708. In an embodiment, vesicles are isolated or captured as described herein, and one or more membrane protein labeling agent is used to detect the vesicles. In many cases, the antigen or other vesicle-moiety that is recognized by the capture and detection agents are interchangeable. As a non-limiting example, consider a vesicle having a cell-of-origin specific antigen on its surface and a cancer-specific antigen on its surface. In one instance, the vesicle can be captured using an antibody to the cell-of-origin specific antigen, e.g., by tethering the capture antibody to a substrate, and then the vesicle is detected using an antibody to the cancer-specific antigen, e.g., by labeling the detection antibody with a fluorescent dye and detecting the fluorescent radiation emitted by the dye.

In another instance, the vesicle can be captured using an antibody to the cancer specific antigen, e.g., by tethering the capture antibody to a substrate, and then the vesicle is detected using an antibody to the cell-of-origin specific antigen, e.g., by labeling the detection antibody with a fluorescent dye and detecting the fluorescent radiation emitted by the dye.

In some embodiments, a same biomarker is recognized by both a capture agent and a detection agent. This scheme can be used depending on the setting. In one embodiment, the biomarker is sufficient to detect a vesicle of interest, e.g., to capture cell-of-origin specific vesicles. In other embodiments, the biomarker is multifunctional, e.g., having both cell-of-origin specific and cancer specific properties. The biomarker can be used in concert with other biomarkers for capture and detection as well.

One method of detecting a biomarker comprises purifying or isolating a heterogeneous population of vesicles from a biological sample, as described above, and performing a sandwich assay. A vesicle in the population can be captured with a capture agent. The capture agent can be a capture antibody, such as a primary antibody. The capture antibody can be bound to a substrate, for example an array, well, or particle. The captured or bound vesicle can be detected with a detection agent, such as a detection antibody. For example, the detection antibody can be for an antigen of the vesicle. The detection antibody can be directly labeled and detected. Alternatively, the detection agent can be indirectly labeled and detected, such as through an enzyme linked secondary antibody that can react with the detection agent. A detection reagent or detection substrate can be added and the reaction detected, such as described in PCT Publication No. WO2009092386. In an illustrative example wherein the capture agent binds Rab-5b and the detection agent binds or detects CD63 or caveolin-1, the capture agent can be an anti-Rab 5b antibody and the detection agent can be an anti-CD63 or anti-caveolin-1 antibody. In some embodiments, the capture agent binds CD9, PSCA, TNFR, CD63, B7H3, MFG-E8, EpCam, Rab, CD81, STEAP, PCSA, PSMA, or 5T4. For example, the capture agent can be an antibody to CD9, PSCA, TNFR, CD63, B7H3, MFG-E8, EpCam, Rab, CD81, STEAP, PCSA, PSMA, or 5T4. The capture agent can also be an antibody to MFG-E8, Annexin V, Tissue Factor, DR3, STEAP, epha2, TMEM211, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, or TETS. The detection agent can be an agent that binds or detects CD63, CD9, CD81, B7H3, or EpCam, such as a detection antibody to CD63, CD9, CD81, B7H3, or EpCam. Various combinations of capture and/or detection agents can be used in concert. In an embodiment, the capture agents comprise PCSA, PSMA, B7H3 and optionally EpCam, and the detection agents comprise one or more general vesicle biomarker, e.g., a tetraspanin such as CD9, CD63 and/or CD81. In another embodiment, the capture agents comprise TMEM211 and CD24, and the detection agents comprise one or more tetraspanin such as CD9, CD63 and CD81. In another embodiment, the capture agents comprise CD66 and EpCam, and the detection agents comprise one or more tetraspanin such as CD9, CD63 and CD81. Increasing numbers of such tetraspanins and/or other general vesicle markers can improve the detection signal in some cases. Proteins or other circulating biomarkers can also be detected using sandwich approaches. The captured vesicles can be collected and used to analyze the payload contained therein, e.g., mRNA, microRNAs, DNA and soluble protein.

In some embodiments, the capture or detection agents recognize one or more of CD9, HSP70, Gal3, MIS, EGFR, ER, ICB3, CD63, B7H4, MUC1, DLL4, CD81, ERB3, VEGF, BCA225, BRCA, CA125, CD174, CD24, ERB2, NGAL, GPR30, CYFRA21, CD31, cMET, MUC2 or ERB4. In some embodiments, the capture or detection agents recognize one or more of CD9, EphA2, EGFR, B7H3, PSMA, PCSA, CD63, STEAP, STEAP, CD81, B7H3, STEAP1, ICAM1 (CD54), PSMA, A33, DR3, CD66e, MFG-8e, EphA2, Hepsin, TMEM211, EphA2, TROP-2, EGFR, Mammoglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, NK-2, EpCam, NGAL, NK-1R, PSMA, 5T4, PAI-1, and CD45. In still other embodiments, the capture or detection agents recognize one or more of CD9, MIS Rii, ER, CD63, MUC1, HER3, STAT3, VEGFA, BCA, CA125, CD24, EPCAM, and ERB B4. The capture or detection agents can recognize one or more of Gal3 and BRCA. In some embodiments, the capture and/or detection agents recognize one or more of A33, APC, BDNF, CD10, CD24, CD63, CD66 CEA, CD81, CDADC1, C-Erb, DR3, EGFR, EphA2, FRT, GAL3, GDF15, GPR30, GRO-1, MACC-1, MMP7, MMP9, MS4A1, MUC1, MUC2, N-gal, OPN, P53, PCSA, PRL, SCRN1, SPR, TFF3, TGM2, TIMP-1, TMEM211, TrKB, TROP2, tsg 101, TWEAK, and UNC93A. In another embodiment, the capture and/or detection agents recognize one or more of A33, APC, B7H3, BDNF, CD10, CD24, CD3, CD63, CD66e, CD81, CD9, CDADC1, C-ERBB2, CRP, CXCL12, EpCam, Ferritin, Gal3, GPCR GRP110, Gro-alpha, Haptoglobin (HAP), HSP70, iC3b, LDH, MACC1, MMP7, MMP9, MS4A1, MUC1, MUC2, NCAM, NDUFB7, NGAL, OPN, PGP9.5, Seprase, SPB, SPC, TFF3, TGM2, TIMP1, TMEM211, TrkB, TWEAK, and UNC93. The capture and/or detection agents can recognize one or more of EPHA2, CD24, EGFR, and/or CEA. In an embodiment, the capture and/or detection agents recognize one or more of A33, ADAM28, AQP5, B7H3, CABYR, CD10, CD24, CD63, CD81, CD9, CEACAM, CHI3L1, DLL4, DR3, EGFR, EpCam, EPHA2, Gal3, GPCR GPR110, iC3b, Mesothelin, MUC1, MUC17, MUC2, NDUFB7, NGAL, NSE, Osteopontin, P2RX7, PCSA, PGP9.5, PSMA, PTP, SPA, SPB, SPC, TMEM211, TPA, TROP2, and UNC93a. The capture and/or detection agents can recognize one or more of ANNEXIN1, ANNEXIN V, ASPH, AURKB, B7H3, BMP2, BRCA1, BTUB, CCL2, CD151, CD45, CD63, CD81, CD9, CEA, CEACAM, CENPH, CKS1, CRP, CYTO 18, CYTO 19, CYTO 7, EGFR, EPCAM, ERB2, FSHR, FTH1, GPCR (GRP 110), HCG, HIF, HLA, INGA3, INTG b4, KRAS, LAMP2, M2PK, MMP1, MMP9, MS4A1, MUC1, MUC2, NACC1, NAP2, NCAM, NSE, Osteopontin, P27, P53, PAN ADH, PCSA, PGP9, PNT, PRO GRP, PSMA, PTH1R, RACK1, SFTPC, SNAIL, SPA, SPD, TGM2, TIMP, TRIM29, TSPAN1, TWIST1, UNCR3, and VEGF. For example, the capture and/or detection agents can be binding agents for CENPH, PRO GRP and MMP9. One or more of these markers can be used as a capture and/or detection agent for characterizing a cancer, e.g., a lung cancer.

In some embodiments, the capture agent binds or targets EpCam, B7H3 or CD24, and the one or more biomarkers detected on the vesicle are CD9 and/or CD63. In one embodiment, the capture agent binds or targets EpCam, and the one or more biomarkers detected on the vesicle are CD9, EpCam and/or CD81. The single capture agent can be selected from CD9, PSCA, TNFR, CD63, B7H3, MFG-E8, EpCam, Rab, CD81, STEAP, PCSA, PSMA, or 5T4. The single capture agent can also be an antibody to DR3, STEAP, epha2, TMEM211, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, MFG-E8, TF, Annexin V or TETS. In some embodiments, the single capture agent is selected from PCSA, PSMA, B7H3, CD81, CD9 and CD63.

In other embodiments, the capture agent targets PCSA, and the one or more biomarkers detected on the captured vesicle are B7H3 and/or PSMA. In other embodiments, the capture agent targets PSMA, and the one or more biomarkers detected on the captured vesicle are B7H3 and/or PCSA. In other embodiments, the capture agent targets B7H3, and the one or more biomarkers detected on the captured vesicle are PSMA and/or PCSA. In yet other embodiments, the capture agent targets CD63 and the one or more biomarkers detected on the vesicle are CD81, CD83, CD9 and/or CD63. The different capture agent and biomarker combinations disclosed herein can be used to characterize a phenotype, such as detecting, diagnosing or prognosing a disease, e.g., a cancer. In some embodiments, vesicles are analyzed to characterize prostate cancer using a capture agent targeting EpCam and detection of CD9 and CD63; a capture agent targeting PCSA and detection of B7H3 and PSMA; or a capture agent of CD63 and detection of CD81. In other embodiments, vesicles are used to characterize colon cancer using capture agent targeting CD63 and detection of CD63, or a capture agent targeting CD9 coupled with detection of CD63. One of skill will appreciate that targets of capture agents and detection agents can be used interchangeably. In an illustrative example, consider a capture agent targeting PCSA and detection agents targeting B7H3 and PSMA. Because all of these markers are useful for detecting PCa derived vesicles, B7H3 or PSMA could be targeted by the capture agent and PCSA could be recognized by a detection agent. For example, in some embodiments, the detection agent targets PCSA, and one or more biomarkers used to capture the vesicle comprise B7H3 and/or PSMA. In other embodiments, the detection agent targets PSMA, and the one or more biomarkers used to capture the vesicle comprise B7H3 and/or PCSA. In other embodiments, the detection agent targets B7H3, and the one or more biomarkers used to capture the vesicle comprise PSMA and/or PCSA. In some embodiments, the invention provides a method of detecting prostate cancer cells in bodily fluid using capture agents and/or detection agents to PSMA, B7H3 and/or PCSA. The bodily fluid can comprise blood, including serum or plasma. The bodily fluid can comprise ejaculate or sperm. In further embodiments, the methods of detecting prostate cancer further use capture agents and/or detection agents to CD81, CD83, CD9 and/or CD63. The method further provides a method of characterizing a GI disorder, comprising capturing vesicles with one or more of DR3, STEAP, epha2, TMEM211, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, and TETS, and detecting the captured vesicles with one or more general vesicle antigen, such as CD81, CD63 and/or CD9. Additional agents can improve the test performance, e.g., improving test accuracy or AUC, either by providing additional biological discriminatory power and/or by reducing experimental noise.

Techniques of detecting biomarkers for use with the invention include the use of a planar substrate such as an array (e.g., biochip or microarray), with molecules immobilized to the substrate as capture agents that facilitate the detection of a particular biosignature. The array can be provided as part of a kit for assaying one or more biomarkers or vesicles. A molecule that identifies the biomarkers described above and shown in FIGS. 3-60, as well as antigens in FIG. 1, can be included in an array for detection and diagnosis of diseases including presymptomatic diseases. In some embodiments, an array comprises a custom array comprising biomolecules selected to specifically identify biomarkers of interest. Customized arrays can be modified to detect biomarkers that increase statistical performance, e.g., additional biomolecules that identifies a biosignature which lead to improved cross-validated error rates in multivariate prediction models (e.g., logistic regression, discriminant analysis, or regression tree models). In some embodiments, customized array(s) are constructed to study the biology of a disease, condition or syndrome and profile biosignatures in defined physiological states. Markers for inclusion on the customized array be chosen based upon statistical criteria, e.g., having a desired level of statistical significance in differentiating between phenotypes or physiological states. In some embodiments, standard significance of p-value=0.05 is chosen to exclude or include biomolecules on the microarray. The p-values can be corrected for multiple comparisons. As an illustrative example, nucleic acids extracted from samples from a subject with or without a disease can be hybridized to a high density microarray that binds to thousands of gene sequences. Nucleic acids whose levels are significantly different between the samples with or without the disease can be selected as biomarkers to distinguish samples as having the disease or not. A customized array can be constructed to detect the selected biomarkers. In some embodiments, customized arrays comprise low density microarrays, which refer to arrays with lower number of addressable binding agents, e.g., tens or hundreds instead of thousands. Low density arrays can be formed on a substrate. In some embodiments, customizable low density arrays use PCR amplification in plate wells, e.g., TaqMan® Gene Expression Assays (Applied Biosystems by Life Technologies Corporation, Carlsbad, Calif.).

A planar array generally contains addressable locations (e.g., pads, addresses, or micro-locations) of biomolecules in an array format. The size of the array will depend on the composition and end use of the array. Arrays can be made containing from 2 different molecules to many thousands. Generally, the array comprises from two to as many as 100,000 or more molecules, depending on the end use of the array and the method of manufacture. A microarray for use with the invention comprises at least one biomolecule that identifies or captures a biomarker present in a biosignature of interest, e.g., a microRNA or other biomolecule or vesicle that makes up the biosignature. In some arrays, multiple substrates are used, either of different or identical compositions. Accordingly, planar arrays may comprise a plurality of smaller substrates.

The present invention can make use of many types of arrays for detecting a biomarker, e.g., a biomarker associated with a biosignature of interest. Useful arrays or microarrays include without limitation DNA microarrays, such as cDNA microarrays, oligonucleotide microarrays and SNP microarrays, microRNA arrays, protein microarrays, antibody microarrays, tissue microarrays, cellular microarrays (also called transfection microarrays), chemical compound microarrays, and carbohydrate arrays (glycoarrays). These arrays are described in more detail above. In some embodiments, microarrays comprise biochips that provide high-density immobilized arrays of recognition molecules (e.g., antibodies), where biomarker binding is monitored indirectly (e.g., via fluorescence). FIG. 2A shows an illustrative configuration in which capture antibodies against a vesicle antigen of interest are tethered to a surface. The captured vesicles are then detected using detector antibodies against the same or different vesicle antigens of interest. The capture antibodies can be substituted with tethered aptamers as available and desirable. Fluorescent detectors are shown. Other detectors can be used similarly, e.g., enzymatic reaction, detectable nanoparticles, radiolabels, and the like. In other embodiments, an array comprises a format that involves the capture of proteins by biochemical or intermolecular interaction, coupled with detection by mass spectrometry (MS). The vesicles can be eluted from the surface and the payload therein, e.g., microRNA, can be analyzed.

An array or microarray that can be used to detect one or more biomarkers of a biosignature can be made according to the methods described in U.S. Pat. Nos. 6,329,209; 6,365,418; 6,406,921; 6,475,808; and 6,475,809, and U.S. patent application Ser. No. 10/884,269, each of which is herein incorporated by reference in its entirety. Custom arrays to detect specific selections of sets of biomarkers described herein can be made using the methods described in these patents. Commercially available microarrays can also be used to carry out the methods of the invention, including without limitation those from Affymetrix (Santa Clara, Calif.), Illumina (San Diego, Calif.), Agilent (Santa Clara, Calif.), Exiqon (Denmark), or Invitrogen (Carlsbad, Calif.). Custom and/or commercial arrays include arrays for detection proteins, nucleic acids, and other biological molecules and entities (e.g., cells, vesicles, virii) as described herein.

In some embodiments, molecules to be immobilized on an array comprise proteins or peptides. One or more types of proteins may be immobilized on a surface. In certain embodiments, the proteins are immobilized using methods and materials that minimize the denaturing of the proteins, that minimize alterations in the activity of the proteins, or that minimize interactions between the protein and the surface on which they are immobilized.

Array surfaces useful may be of any desired shape, form, or size. Non-limiting examples of surfaces include chips, continuous surfaces, curved surfaces, flexible surfaces, films, plates, sheets, or tubes. Surfaces can have areas ranging from approximately a square micron to approximately 500 cm². The area, length, and width of surfaces may be varied according to the requirements of the assay to be performed. Considerations may include, for example, ease of handling, limitations of the material(s) of which the surface is formed, requirements of detection systems, requirements of deposition systems (e.g., arrayers), or the like.

In certain embodiments, it is desirable to employ a physical means for separating groups or arrays of binding islands or immobilized biomolecules: such physical separation facilitates exposure of different groups or arrays to different solutions of interest. Therefore, in certain embodiments, arrays are situated within microwell plates having any number of wells. In such embodiments, the bottoms of the wells may serve as surfaces for the formation of arrays, or arrays may be formed on other surfaces and then placed into wells. In certain embodiments, such as where a surface without wells is used, binding islands may be formed or molecules may be immobilized on a surface and a gasket having holes spatially arranged so that they correspond to the islands or biomolecules may be placed on the surface. Such a gasket is preferably liquid tight. A gasket may be placed on a surface at any time during the process of making the array and may be removed if separation of groups or arrays is no longer necessary.

In some embodiments, the immobilized molecules can bind to one or more biomarkers or vesicles present in a biological sample contacting the immobilized molecules. In some embodiments, the immobilized molecules modify or are modified by molecules present in the one or more vesicles contacting the immobilized molecules. Contacting the sample typically comprises overlaying the sample upon the array.

Modifications or binding of molecules in solution or immobilized on an array can be detected using detection techniques known in the art. Examples of such techniques include immunological techniques such as competitive binding assays and sandwich assays; fluorescence detection using instruments such as confocal scanners, confocal microscopes, or CCD-based systems and techniques such as fluorescence, fluorescence polarization (FP), fluorescence resonant energy transfer (FRET), total internal reflection fluorescence (TIRF), fluorescence correlation spectroscopy (FCS); colorimetric/spectrometric techniques; surface plasmon resonance, by which changes in mass of materials adsorbed at surfaces are measured; techniques using radioisotopes, including conventional radioisotope binding and scintillation proximity assays (SPA); mass spectroscopy, such as matrix-assisted laser desorption/ionization mass spectroscopy (MALDI) and MALDI-time of flight (TOF) mass spectroscopy; ellipsometry, which is an optical method of measuring thickness of protein films; quartz crystal microbalance (QCM), a very sensitive method for measuring mass of materials adsorbing to surfaces; scanning probe microscopies, such as atomic force microscopy (AFM), scanning force microscopy (SFM) or scanning electron microscopy (SEM); and techniques such as electrochemical, impedance, acoustic, microwave, and IR/Raman detection. See, e.g., Mere L, et al., “Miniaturized FRET assays and microfluidics: key components for ultra-high-throughput screening,” Drug Discovery Today 4(8):363-369 (1999), and references cited therein; Lakowicz J R, Principles of Fluorescence Spectroscopy, 2nd Edition, Plenum Press (1999), or Jain K K: Integrative Omics, Pharmacoproteomics, and Human Body Fluids. In: Thongboonkerd V, ed., ed. Proteomics of Human Body Fluids: Principles, Methods and Applications. Volume 1: Totowa, N.J.: Humana Press, 2007, each of which is herein incorporated by reference in its entirety.

Microarray technology can be combined with mass spectroscopy (MS) analysis and other tools. Electrospray interface to a mass spectrometer can be integrated with a capillary in a microfluidics device. For example, one commercially available system contains eTag reporters that are fluorescent labels with unique and well-defined electrophoretic mobilities; each label is coupled to biological or chemical probes via cleavable linkages. The distinct mobility address of each eTag reporter allows mixtures of these tags to be rapidly deconvoluted and quantitated by capillary electrophoresis. This system allows concurrent gene expression, protein expression, and protein function analyses from the same sample Jain K K: Integrative Omics, Pharmacoproteomics, and Human Body Fluids. In: Thongboonkerd V, ed., ed. Proteomics of Human Body Fluids: Principles, Methods and Applications. Volume 1: Totowa, N.J.: Humana Press, 2007, which is herein incorporated by reference in its entirety.

A biochip can include components for a microfluidic or nanofluidic assay. A microfluidic device can be used for isolating or analyzing biomarkers, such as determining a biosignature. Microfluidic systems allow for the miniaturization and compartmentalization of one or more processes for isolating, capturing or detecting a vesicle, detecting a microRNA, detecting a circulating biomarker, detecting a biosignature, and other processes. The microfluidic devices can use one or more detection reagents in at least one aspect of the system, and such a detection reagent can be used to detect one or more biomarkers. In one embodiment, the device detects a biomarker on an isolated or bound vesicle. Various probes, antibodies, proteins, or other binding agents can be used to detect a biomarker within the microfluidic system. The detection agents may be immobilized in different compartments of the microfluidic device or be entered into a hybridization or detection reaction through various channels of the device.

A vesicle in a microfluidic device can be lysed and its contents detected within the microfluidic device, such as proteins or nucleic acids, e.g., DNA or RNA such as miRNA or mRNA. The nucleic acid may be amplified prior to detection, or directly detected, within the microfluidic device. Thus microfluidic system can also be used for multiplexing detection of various biomarkers. In an embodiment, vesicles are captured within the microfluidic device, the captured vesicles are lysed, and a biosignature of microRNA from the vesicle payload is determined. The biosignature can further comprise the capture agent used to capture the vesicle.

Nanofabrication techniques are opening up the possibilities for biosensing applications that rely on fabrication of high-density, precision arrays, e.g., nucleotide-based chips and protein arrays otherwise know as heterogeneous nanoarrays. Nanofluidics allows a further reduction in the quantity of fluid analyte in a microchip to nanoliter levels, and the chips used here are referred to as nanochips. (See, e.g., Unger M et al., Biotechniques 1999; 27(5):1008-14, Kartalov E P et al., Biotechniques 2006; 40(1):85-90, each of which are herein incorporated by reference in their entireties.) Commercially available nanochips currently provide simple one step assays such as total cholesterol, total protein or glucose assays that can be run by combining sample and reagents, mixing and monitoring of the reaction. Gel-free analytical approaches based on liquid chromatography (LC) and nanoLC separations (Cutillas et al. Proteomics, 2005; 5:101-112 and Cutillas et al., Mol Cell Proteomics 2005; 4:1038-1051, each of which is herein incorporated by reference in its entirety) can be used in combination with the nanochips.

An array suitable for identifying a disease, condition, syndrome or physiological status can be included in a kit. A kit can include, as non-limiting examples, one or more reagents useful for preparing molecules for immobilization onto binding islands or areas of an array, reagents useful for detecting binding of a vesicle to immobilized molecules, and instructions for use.

Further provided herein is a rapid detection device that facilitates the detection of a particular biosignature in a biological sample. The device can integrate biological sample preparation with polymerase chain reaction (PCR) on a chip. The device can facilitate the detection of a particular biosignature of a vesicle in a biological sample, and an example is provided as described in Pipper et al., Angewandte Chemie, 47(21), p. 3900-3904 (2008), which is herein incorporated by reference in its entirety. A biosignature can be incorporated using micro-/nano-electrochemical system (MEMS/NEMS) sensors and oral fluid for diagnostic applications as described in Li et al., Adv Dent Res 18(1): 3-5 (2005), which is herein incorporated by reference in its entirety.

As an alternative to planar arrays, assays using particles, such as bead based assays as described herein, can be used in combination with flow cytometry. Multiparametric assays or other high throughput detection assays using bead coatings with cognate ligands and reporter molecules with specific activities consistent with high sensitivity automation can be used. In a bead based assay system, a binding agent for a biomarker or vesicle, such as a capture agent (e.g. capture antibody), can be immobilized on an addressable microsphere. Each binding agent for each individual binding assay can be coupled to a distinct type of microsphere (i.e., microbead) and the assay reaction takes place on the surface of the microsphere, such as depicted in FIG. 63B. A binding agent for a vesicle can be a capture antibody coupled to a bead. Dyed microspheres with discrete fluorescence intensities are loaded separately with their appropriate binding agent or capture probes. The different bead sets carrying different binding agents can be pooled as necessary to generate custom bead arrays. Bead arrays are then incubated with the sample in a single reaction vessel to perform the assay. Examples of microfluidic devices that may be used, or adapted for use with the invention, include but are not limited to those described herein.

Product formation of the biomarker with an immobilized capture molecule or binding agent can be detected with a fluorescence based reporter system (see for example, FIG. 63A-B). The biomarker can either be labeled directly by a fluorophore or detected by a second fluorescently labeled capture biomolecule. The signal intensities derived from captured biomarkers can be measured in a flow cytometer. The flow cytometer can first identify each microsphere by its individual color code. For example, distinct beads can be dyed with discrete fluorescence intensities such that each bead with a different intensity has a different binding agent. The beads can be labeled or dyed with at least 2 different labels or dyes. In some embodiments, the beads are labeled with at least 3, 4, 5, 6, 7, 8, 9, or 10 different labels. The beads with more than one label or dye can also have various ratios and combinations of the labels or dyes. The beads can be labeled or dyed externally or may have intrinsic fluorescence or signaling labels.

The amount of captured biomarkers on each individual bead can be measured by the second color fluorescence specific for the bound target. This allows multiplexed quantitation of multiple targets from a single sample within the same experiment. Sensitivity, reliability and accuracy are compared or can be improved to standard microtiter ELISA procedures. An advantage of a bead-based system is the individual coupling of the capture biomolecule or binding agent for a vesicle to distinct microspheres provides multiplexing capabilities. For example, as depicted in FIG. 63C, a combination of 5 different biomarkers to be detected (detected by antibodies to antigens such as CD63, CD9, CD81, B7H3, and EpCam) and 20 biomarkers for which to capture a vesicle, (using capture antibodies, such as antibodies to CD9, PSCA, TNFR, CD63, B7H3, MFG-E8, EpCam, Rab, CD81, STEAP, PCSA, PSMA, 5T4, and/or CD24) can result in approximately 100 combinations to be detected. As shown in FIG. 63C as “EpCam 2x,” “CD63 2X,” multiple antibodies to a single target can be used to probe detection against various epitopes. In another example, multiplex analysis comprises capturing a vesicle using a binding agent to CD24 and detecting the captured vesicle using a binding agent for CD9, CD63, and/or CD81. The captured vesicles can be detected using a detection agent such as an antibody. The detection agents can be labeled directly or indirectly, as described herein.

Any appropriate panel of vesicle biomarkers disclosed herein can be used in multiplex analysis. For example, one or more of the following biomarkers can also be used in multiplex analysis: CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, and TNFR. In another example, one or more of the following biomarkers can also be used in multiplex analysis: 5T4, A33, B7H3, B7H4, BCA, BCA225, BRCA, CA125, CD174, CD24, CD31, CD45, CD63, CD66e, CD81, CD9, cMET, CYFRA21, DLL4, DR3, EGFR, EpCam, EphA2, ER, ERB B4, ERB2, ERB3, ERB4, Gal3, GPR30, Hepsin, HER3, HSP70, ICAM1 (CD54), ICB3, Mammoglobin, MFG-8e, MIS, MIS Rii, MUC1, MUC2, NGAL, NK-1R, NK-2, NPGP/NPFF2, PAI-1, PCSA, PSCA, PSMA, STAT3, STEAP1 (STEAP), TROP-2, VEGF, and VEGFA.

Any appropriate panel of vesicle biomarkers disclosed herein can be used in multiplex analysis. In some embodiments, one or more of the following markers is assessed for multiplex analysis: A33, APC, BDNF, CD10, CD24, CD63, CD66 CEA, CD81, CDADC1, C-Erb, DR3, EGFR, EphA2, FRT, GAL3, GDF15, GPR30, GRO-1, MACC-1, MMP7, MMP9, MS4A1, MUC1, MUC2, N-gal, OPN, P53, PCSA, PRL, SCRN1, SPR, TFF3, TGM2, TIMP-1, TMEM211, TrKB, TROP2, tsg 101, TWEAK, and UNC93A. In another embodiment, one or more of the following markers is assessed for multiplex analysis: A33, APC, B7H3, BDNF, CD10, CD24, CD3, CD63, CD66e, CD81, CD9, CDADC1, C-ERBB2, CRP, CXCL12, EpCam, Ferritin, Gal3, GPCR GRP110, Gro-alpha, Haptoglobin (HAP), HSP70, iC3b, LDH, MACC1, MMP7, MMP9, MS4A1, MUC1, MUC2, NCAM, NDUFB7, NGAL, OPN, PGP9.5, Seprase, SPB, SPC, TFF3, TGM2, TIMP1, TMEM211, TrkB, TWEAK, and UNC93. One or more of the following markers can be assessed for multiplex analysis: EPHA2, CD24, EGFR, and/or CEA. In an embodiment, one or more of the following markers is assessed for multiplex analysis: A33, ADAM28, AQP5, B7H3, CABYR, CD10, CD24, CD63, CD81, CD9, CEACAM, CHI3L1, DLL4, DR3, EGFR, EpCam, EPHA2, ER, ERB B4, Gal3, GPCR GPR110, iC3b, Mesothelin, MUC1, MUC17, MUC2, NDUFB7, NGAL, NSE, Osteopontin, P2RX7, PCSA, PGP9.5, PSMA, PTP, SPA, SPB, SPC, TMEM211, TPA, TROP2, and UNC93a. In another embodiment, one or more of the following markers is assessed for multiplex analysis: ERBB3, ERBB4, Gal3, GPR30, Hepsin, HER3, HSP70, ICAM1 (CD54), ICB3, Mammoglobin, MFG-8e, MIS, MIS Rii, MUC1, MUC2, NGAL, NK-1R, NK-2, NPGP/NPFF2, PAI-1, PCSA, PSCA, PSMA, STAT3, STEAP1 (STEAP), TROP-2 and VEGFA. In an embodiment, one or more of the following markers is used for multiplex analysis: ANNEXIN1, ANNEXIN V, ASPH, AURKB, B7H3, BMP2, BRCA1, BTUB, CCL2, CD151, CD45, CD63, CD81, CD9, CEA, CEACAM, CENPH, CKS1, CRP, CYTO 18, CYTO 19, CYTO 7, EGFR, EPCAM, ERB2, FSHR, FTH1, GPCR (GRP 110), HCG, HIF, HLA, INGA3, INTG b4, KRAS, LAMP2, M2PK, MMP1, MMP9, MS4A1, MUC1, MUC2, NACC1, NAP2, NCAM, NSE, Osteopontin, P27, P53, PAN ADH, PCSA, PGP9, PNT, PRO GRP, PSMA, PTH1R, RACK1, SFTPC, SNAIL, SPA, SPD, TGM2, TIMP, TRIM29, TSPAN1, TWIST1, UNCR3, and VEGF. For example, multiplex analysis can comprise assessment of CENPH, PRO GRP and MMP9.

Multiplexing of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75 or 100 different biomarkers may be performed. For example, an assay of a heterogeneous population of vesicles can be performed with a plurality of particles that are differentially labeled. There can be at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75 or 100 differentially labeled particles. The particles may be externally labeled, such as with a tag, or they may be intrinsically labeled. Each differentially labeled particle can be coupled to a capture agent, such as a binding agent, for a vesicle, resulting in capture of a vesicle. The multiple capture agents can be selected to characterize a phenotype of interest, including capture agents against general vesicle biomarkers, cell-of-origin specific biomarkers, and disease biomarkers. One or more biomarkers of the captured vesicle can then be detected by a plurality of binding agents. The binding agent can be directly labeled to facilitate detection. Alternatively, the binding agent is labeled by a secondary agent. For example, the binding agent may be an antibody for a biomarker on the vesicle. The binding agent is linked to biotin. A secondary agent comprises streptavidin linked to a reporter and can be added to detect the biomarker. In some embodiments, the captured vesicle is assayed for at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75 or 100 different biomarkers. For example, multiple detectors, i.e., detection of multiple biomarkers of a captured vesicle or population of vesicles, can increase the signal obtained, permitted increased sensitivity, specificity, or both, and the use of smaller amounts of samples. For example, detection with more than one general vesicle marker can improve the signal as compared to using a lesser number of detection markers, such as a single marker. To illustrate, detection of vesicles with labeled binding agents to two or three of CD9, CD63 and CD81 can improve the signal compared to detection with any one of the tetraspanins individually.

An immunoassay based method or sandwich assay can also be used to detect a biomarker of a vesicle. An example includes ELISA. A binding agent or capture agent can be bound to a well. For example an antibody to an antigen of a vesicle can be attached to a well. A biomarker on the captured vesicle can be detected based on the methods described herein. FIG. 63A shows an illustrative schematic for a sandwich-type of immunoassay. The capture antibody can be against a vesicle antigen of interest, e.g., a general vesicle biomarker, a cell-of-origin marker, or a disease marker. In the figure, the captured vesicles are detected using fluorescently labeled antibodies against vesicle antigens of interest. Multiple capture antibodies can be used, e.g., in distinguishable addresses on an array or different wells of an immunoassay plate. The detection antibodies can be against the same antigen as the capture antibody, or can be directed against other markers. The capture antibodies can be substituted with alternate binding agents, such as tethered aptamers or lectins, and/or the detector antibodies can be similarly substituted, e.g., with detectable (e.g., labeled) aptamers, lectins or other binding proteins or entities. In an embodiment, one or more capture agents to a general vesicle biomarker, a cell-of-origin marker, and/or a disease marker are used along with detection agents against general vesicle biomarker, such as tetraspanin molecules including without limitation one or more of CD9, CD63 and CD81.

FIG. 63D presents an illustrative schematic for analyzing vesicles according to the methods of the invention. Capture agents are used to capture vesicles, detectors are used to detect the captured vesicles, and the level or presence of the captured and detected antibodies is used to characterize a phenotype. Capture agents, detectors and characterizing phenotypes can be any of those described herein. For example, capture agents include antibodies or aptamers tethered to a substrate that recognize a vesicle antigen of interest, detectors include labeled antibodies or aptamers to a vesicle antigen of interest, and characterizing a phenotype includes a diagnosis, prognosis, or theranosis of a disease. In the scheme shown in FIG. 63D i), a population of vesicles is captured with one or more capture agents against general vesicle biomarkers (6300). The captured vesicles are then labeled with detectors against cell-of-origin biomarkers (6301) and/or disease specific biomarkers (6302). If only cell-of-origin detectors are used (6301), the biosignature used to characterize the phenotype (6303) can include the general vesicle markers (6300) and the cell-of-origin biomarkers (6301). If only disease detectors are used (6302), the biosignature used to characterize the phenotype (6303) can include the general vesicle markers (6300) and the disease biomarkers (6302). Alternately, detectors are used to detect both cell-of-origin biomarkers (6301) and disease specific biomarkers (6302). In this case, the biosignature used to characterize the phenotype (6303) can include the general vesicle markers (6300), the cell-of-origin biomarkers (6301) and the disease biomarkers (6302). The biomarkers combinations are selected to characterize the phenotype of interest and can be selected from the biomarkers and phenotypes described herein.

In the scheme shown in FIG. 63D ii), a population of vesicles is captured with one or more capture agents against cell-of-origin biomarkers (6310) and/or disease biomarkers (6311). The captured vesicles are then detected using detectors against general vesicle biomarkers (6312). If only cell-of-origin capture agents are used (6310), the biosignature used to characterize the phenotype (6313) can include the cell-of-origin biomarkers (6310) and the general vesicle markers (6312). If only disease biomarker capture agents are used (6311), the biosignature used to characterize the phenotype (6313) can include the disease biomarkers (6311) and the general vesicle biomarkers (6312). Alternately, capture agents to one or more cell-of-origin biomarkers (6310) and one or more disease specific biomarkers (6311) are used to capture vesicles. In this case, the biosignature used to characterize the phenotype (6313) can include the cell-of-origin biomarkers (6310), the disease biomarkers (6311), and the general vesicle markers (6313). The biomarkers combinations are selected to characterize the phenotype of interest and can be selected from the biomarkers and phenotypes described herein.

Biomarkers comprising vesicle payload can be analyzed to characterize a phenotype. Payload comprises the biological entities contained within a vesicle membrane. These entities include without limitation nucleic acids, e.g., mRNA, microRNA, or DNA fragments; protein, e.g., soluble and membrane associated proteins; carbohydrates; lipids; metabolites; and various small molecules, e.g., hormones. The payload can be part of the cellular milieu that is encapsulated as a vesicle is formed in the cellular environment. In some embodiments of the invention, the payload is analyzed in addition to detecting vesicle surface antigens. Specific populations of vesicles can be captured as described above then the payload in the captured vesicles can be used to characterize a phenotype. For example, vesicles captured on a substrate can be further isolated to assess the payload therein. Alternately, the vesicles in a sample are detected and sorted without capture. The vesicles so detected can be further isolated to assess the payload therein. In an embodiment, vesicle populations are sorted by flow cytometry and the payload in the sorted vesicles is analyzed. In the scheme shown in FIG. 63E iii), a population of vesicles is captured and/or detected (6320) using one or more of cell-of-origin biomarkers (6320), disease biomarkers (6321), and general vesicle markers (6322). The payload of the isolated vesicles is assessed (6323). A biosignature detected within the payload can be used to characterize a phenotype (6324). In a non-limiting example, a vesicle population can be analyzed in a plasma sample from a patient using antibodies against one or more vesicle antigens of interest. The antibodies can be capture antibodies which are tethered to a substrate to isolate a desired vesicle population. Alternately, the antibodies can be directly labeled and the labeled vesicles isolated by sorting with flow cytometry. The presence or level of microRNA or mRNA extracted from the isolated vesicle population can be used to detect a biosignature. The biosignature is then used to diagnose, prognose or theranose the patient.

In other embodiments, vesicle payload is analyzed in a vesicle population without first capturing or detected subpopulations of vesicles. For example, vesicles can be generally isolated from a sample using centrifugation, filtration, chromatography, or other techniques as described herein. The payload of the isolated vesicles can be analyzed thereafter to detect a biosignature and characterize a phenotype. In the scheme shown in FIG. 63E iv), a population of vesicles is isolated (6330) and the payload of the isolated vesicles is assessed (6331). A biosignature detected within the payload can be used to characterize a phenotype (6332). In a non-limiting example, a vesicle population is isolated from a plasma sample from a patient using size exclusion and membrane filtration. The presence or level of microRNA or mRNA extracted from the vesicle population is used to detect a biosignature. The biosignature is then used to diagnose, prognose or theranose the patient.

A peptide or protein biomarker can be analyzed by mass spectrometry or flow cytometry. Proteomic analysis of a vesicle may be carried out by immunocytochemical staining, Western blotting, electrophoresis, SDS-PAGE, chromatography, x-ray crystallography or other protein analysis techniques in accordance with procedures well known in the art. In other embodiments, the protein biosignature of a vesicle may be analyzed using 2 D differential gel electrophoresis as described in, Chromy et al. J Proteome Res, 2004; 3:1120-1127, which is herein incorporated by reference in its entirety, or with liquid chromatography mass spectrometry as described in Zhang et al. Mol Cell Proteomics, 2005; 4:144-155, which is herein incorporated by reference in its entirety. A vesicle may be subjected to activity-based protein profiling described for example, in Berger et al., Am J Pharmacogenomics, 2004; 4:371-381, which is in incorporated by reference in its entirety. In other embodiments, a vesicle may be profiled using nanospray liquid chromatography-tandem mass spectrometry as described in Pisitkun et al., Proc Natl Acad Sci USA, 2004; 101:13368-13373, which is herein incorporated by reference in its entirety. In another embodiment, the vesicle may be profiled using tandem mass spectrometry (MS) such as liquid chromatography/MS/MS (LC-MS/MS) using for example a LTQ and LTQ-FT ion trap mass spectrometer. Protein identification can be determined and relative quantitation can be assessed by comparing spectral counts as described in Smalley et al., J Proteome Res, 2008; 7:2088-2096, which is herein incorporated by reference in its entirety.

The expression of circulating protein biomarkers or protein payload within a vesicle can also be identified. The latter analysis can optionally follow the isolation of specific vesicles using capture agents to capture populations of interest. In an embodiment, immunocytochemical staining is used to analyze protein expression. The sample can be resuspended in buffer, centrifuged at 100×g for example, for 3 minutes using a cytocentrifuge on adhesive slides in preparation for immunocytochemical staining. The cytospins can be air-dried overnight and stored at −80° C. until staining. Slides can then be fixed and blocked with serum-free blocking reagent. The slides can then be incubated with a specific antibody to detect the expression of a protein of interest. In some embodiments, the vesicles are not purified, isolated or concentrated prior to protein expression analysis.

Biosignatures comprising vesicle payload can be characterized by analysis of a metabolite marker or metabolite within the vesicle. Various metabolite-oriented approaches have been described such as metabolite target analyses, metabolite profiling, or metabolic fingerprinting, see for example, Denkert et al., Molecular Cancer 2008; 7: 4598-4617, Ellis et al., Analyst 2006; 8: 875-885, Kuhn et al., Clinical Cancer Research 2007; 24: 7401-7406, Fiehn O., Comp Funct Genomics 2001; 2:155-168, Fancy et al., Rapid Commun Mass Spectrom 20(15): 2271-80 (2006), Lindon et al., Pharm Res, 23(6): 1075-88 (2006), Holmes et al., Anal Chem. 2007 Apr. 1; 79(7):2629-40. Epub 2007 Feb. 27. Erratum in: Anal Chem. 2008 Aug. 1; 80(15):6142-3, Stanley et al., Anal Biochem. 2005 Aug. 15; 343(2): 195-202., Lehtimäki et al., J Biol Chem. 2003 Nov. 14; 278(46):45915-23, each of which is herein incorporated by reference in its entirety.

Peptides can be analyzed by systems described in Jain K K: Integrative Omics, Pharmacoproteomics, and Human Body Fluids. In: Thongboonkerd V, ed., ed. Proteomics of Human Body Fluids: Principles, Methods and Applications. Volume 1: Totowa, N.J.: Humana Press, 2007, which is herein incorporated by reference in its entirety. This system can generate sensitive molecular fingerprints of proteins present in a body fluid as well as in vesicles. Commercial applications which include the use of chromatography/mass spectroscopy and reference libraries of all stable metabolites in the human body, for example Paradigm Genetic's Human Metabolome Project, may be used to determine a metabolite biosignature. Other methods for analyzing a metabolic profile can include methods and devices described in U.S. Pat. No. 6,683,455 (Metabometrix), U.S. Patent Application Publication Nos. 20070003965 and 20070004044 (Biocrates Life Science), each of which is herein incorporated by reference in its entirety. Other proteomic profiling techniques are described in Kennedy, Toxicol Lett 120:379-384 (2001), Berven et al., Curr Pharm Biotechnol 7(3): 147-58 (2006), Conrads et al., Expert Rev Proteomics 2(5): 693-703, Decramer et al., World J Urol 25(5): 457-65 (2007), Decramer et al., Mol Cell Proteomics 7(10): 1850-62 (2008), Decramer et al., Contrib Nephrol, 160: 127-41 (2008), Diamandis, J Proteome Res 5(9): 2079-82 (2006), Immler et al., Proteomics 6(10): 2947-58 (2006), Khan et al., J Proteome Res 5(10): 2824-38 (2006), Kumar et al., Biomarkers 11(5): 385-405 (2006), Noble et al., Breast Cancer Res Treat 104(2): 191-6 (2007), Omenn, Dis Markers 20(3): 131-4 (2004), Powell et al., Expert Rev Proteomics 3(1): 63-74 (2006), Rai et al., Arch Pathol Lab Med, 126(12): 1518-26 (2002), Ramstrom et al., Proteomics, 3(2): 184-90 (2003), Tammen et al., Breast Cancer Res Treat, 79(1): 83-93 (2003), Theodorescu et al., Lancet Oncol, 7(3): 230-40 (2006), or Zurbig et al., Electrophoresis, 27(11): 2111-25 (2006).

For analysis of mRNAs, miRNAs or other small RNAs, the total RNA can be isolated using any known methods for isolating nucleic acids such as methods described in U.S. Patent Application Publication No. 2008132694, which is herein incorporated by reference in its entirety. These include, but are not limited to, kits for performing membrane based RNA purification, which are commercially available. Generally, kits are available for the small-scale (30 mg or less) preparation of RNA from cells and tissues, for the medium scale (250 mg tissue) preparation of RNA from cells and tissues, and for the large scale (1g maximum) preparation of RNA from cells and tissues. Other commercially available kits for effective isolation of small RNA-containing total RNA are available. Such methods can be used to isolate nucleic acids from vesicles.

Alternatively, RNA can be isolated using the method described in U.S. Pat. No. 7,267,950, which is herein incorporated by reference in its entirety. U.S. Pat. No. 7,267,950 describes a method of extracting RNA from biological systems (cells, cell fragments, organelles, tissues, organs, or organisms) in which a solution containing RNA is contacted with a substrate to which RNA can bind and RNA is withdrawn from the substrate by applying negative pressure. Alternatively, RNA may be isolated using the method described in U.S. Patent Application No. 20050059024, which is herein incorporated by reference in its entirety, which describes the isolation of small RNA molecules. Other methods are described in U.S. Patent Application No. 20050208510, 20050277121, 20070238118, each of which is incorporated by reference in its entirety.

In one embodiment, mRNA expression analysis can be carried out on mRNAs from a vesicle isolated from a sample. In some embodiments, the vesicle is a cell-of-origin specific vesicle. An expression pattern generated from a vesicle can be indicative of a given disease state, disease stage, therapy related signature, or physiological condition.

In one embodiment, once the total RNA has been isolated, cDNA can be synthesized and either qRT-PCR assays (e.g. Applied Biosystem's Taqman® assays) for specific mRNA targets can be performed according to manufacturer's protocol, or an expression microarray can be performed to look at highly multiplexed sets of expression markers in one experiment. Methods for establishing gene expression profiles include determining the amount of RNA that is produced by a gene that can code for a protein or peptide. This can be accomplished by quantitative reverse transcriptase PCR (qRT-PCR), competitive RT-PCR, real time RT-PCR, differential display RT-PCR, Northern Blot analysis or other related tests. While it is possible to conduct these techniques using individual PCR reactions, it is also possible to amplify complementary DNA (cDNA) or complementary RNA (cRNA) produced from mRNA and analyze it via microarray.

The level of a miRNA product in a sample can be measured using any appropriate technique that is suitable for detecting mRNA expression levels in a biological sample, including but not limited to Northern blot analysis, RT-PCR, qRT-PCR, in situ hybridization or microarray analysis. For example, using gene specific primers and target cDNA, qRT-PCR enables sensitive and quantitative miRNA measurements of either a small number of target miRNAs (via singleplex and multiplex analysis) or the platform can be adopted to conduct high throughput measurements using 96-well or 384-well plate formats. See for example, Ross J S et al, Oncologist. 2008 May; 13(5):477-93, which is herein incorporated by reference in its entirety. A number of different array configurations and methods for microarray production are known to those of skill in the art and are described in U.S. patents such as: U.S. Pat. No. 5,445,934; 5,532,128; 5,556,752; 5,242,974; 5,384,261; 5,405,783; 5,412,087; 5,424,186; 5,429,807; 5,436,327; 5,472,672; 5,527,681; 5,529,756; 5,545,531; 5,554,501; 5,561,071; 5,571,639; 5,593,839; 5,599,695; 5,624,711; 5,658,734; or 5,700,637; each of which is herein incorporated by reference in its entirety. Other methods of profiling miRNAs are described in Taylor et al., Gynecol Oncol. 2008 July; 110(1): 13-21, Gilad et al, PLoS ONE. 2008 Sep. 5; 3(9):e3148, Lee et al., Annu Rev Pathol. 2008 September 25 and Mitchell et al, Proc Natl Acad Sci USA. 2008 Jul. 29; 105(30):10513-8, Shen R et al, BMC Genomics. 2004 Dec. 14; 5(1):94, Mina L et al, Breast Cancer Res Treat. 2007 June; 103(2):197-208, Zhang L et al, Proc Natl Acad Sci USA. 2008 May 13; 105(19):7004-9, Ross J S et al, Oncologist. 2008 May; 13(5):477-93, Schetter A J et al, JAMA. 2008 Jan. 30; 299(4):425-36, Staudt L M, N Engl J Med 2003; 348:1777-85, Mulligan G et al, Blood. 2007 Apr. 15; 109(8):3177-88. Epub 2006 December 21, McLendon R et al, Nature. 2008 Oct. 23; 455(7216):1061-8, and U.S. Pat. Nos. 5,538,848, 5,723,591, 5,876,930, 6,030,787, 6,258,569, and 5,804,375, each of which is herein incorporated by reference. In some embodiments, arrays of microRNA panels are use to simultaneously query the expression of multiple miRs. The Exiqon mIRCURY LNA microRNA PCR system panel (Exiqon, Inc., Woburn, Mass.) or the TaqMan® MicroRNA Assays and Arrays systems from Applied Biosystems (Foster City, Calif.) can be used for such purposes.

Microarray technology allows for the measurement of the steady-state mRNA or miRNA levels of thousands of transcripts or miRNAs simultaneously thereby presenting a powerful tool for identifying effects such as the onset, arrest, or modulation of uncontrolled cell proliferation. Two microarray technologies, such as cDNA arrays and oligonucleotide arrays can be used. The product of these analyses are typically measurements of the intensity of the signal received from a labeled probe used to detect a cDNA sequence from the sample that hybridizes to a nucleic acid sequence at a known location on the microarray. Typically, the intensity of the signal is proportional to the quantity of cDNA, and thus mRNA or miRNA, expressed in the sample cells. A large number of such techniques are available and useful. Methods for determining gene expression can be found in U.S. Pat. No. 6,271,002 to Linsley, et al.; U.S. Pat. No. 6,218,122 to Friend, et al.; U.S. Pat. No. 6,218,114 to Peck et al.; or U.S. Pat. No. 6,004,755 to Wang, et al., each of which is herein incorporated by reference in its entirety.

Analysis of an expression level can be conducted by comparing such intensities. This can be performed by generating a ratio matrix of the expression intensities of genes in a test sample versus those in a control sample. The control sample may be used as a reference, and different references to account for age, ethnicity and sex may be used. Different references can be used for different conditions or diseases, as well as different stages of diseases or conditions, as well as for determining therapeutic efficacy.

For instance, the gene expression intensities of mRNA or miRNAs derived from a diseased tissue, including those isolated from vesicles, can be compared with the expression intensities of the same entities in normal tissue of the same type (e.g., diseased breast tissue sample versus normal breast tissue sample). A ratio of these expression intensities indicates the fold-change in gene expression between the test and control samples. Alternatively, if vesicles are not normally present in from normal tissues (e.g. breast) then absolute quantitation methods, as is known in the art, can be used to define the number of miRNA molecules present without the requirement of miRNA or mRNA isolated from vesicles derived from normal tissue.

Gene expression profiles can also be displayed in a number of ways. A common method is to arrange raw fluorescence intensities or ratio matrix into a graphical dendogram where columns indicate test samples and rows indicate genes. The data is arranged so genes that have similar expression profiles are proximal to each other. The expression ratio for each gene is visualized as a color. For example, a ratio less than one (indicating down-regulation) may appear in the blue portion of the spectrum while a ratio greater than one (indicating upregulation) may appear as a color in the red portion of the spectrum. Commercially available computer software programs are available to display such data.

mRNAs or miRNAs that are considered differentially expressed can be either over expressed or under expressed in patients with a disease relative to disease free individuals. Over and under expression are relative terms meaning that a detectable difference (beyond the contribution of noise in the system used to measure it) is found in the amount of expression of the mRNAs or miRNAs relative to some baseline. In this case, the baseline is the measured mRNA/miRNA expression of a non-diseased individual. The mRNA/miRNA of interest in the diseased cells can then be either over or under expressed relative to the baseline level using the same measurement method. Diseased, in this context, refers to an alteration of the state of a body that interrupts or disturbs, or has the potential to disturb, proper performance of bodily functions as occurs with the uncontrolled proliferation of cells. Someone is diagnosed with a disease when some aspect of that person's genotype or phenotype is consistent with the presence of the disease. However, the act of conducting a diagnosis or prognosis includes the determination of disease/status issues such as determining the likelihood of relapse or metastasis and therapy monitoring. In therapy monitoring, clinical judgments are made regarding the effect of a given course of therapy by comparing the expression of genes over time to determine whether the mRNA/miRNA expression profiles have changed or are changing to patterns more consistent with normal tissue.

Levels of over and under expression are distinguished based on fold changes of the intensity measurements of hybridized microarray probes. A 2× difference is preferred for making such distinctions or a p-value less than 0.05. That is, before an mRNA/miRNA is the to be differentially expressed in diseased/relapsing versus normal/non-relapsing cells, the diseased cell is found to yield at least 2 times more, or 2 times less intensity than the normal cells. The greater the fold difference, the more preferred is use of the gene as a diagnostic or prognostic tool. mRNA/miRNAs selected for the expression profiles of the instant invention have expression levels that result in the generation of a signal that is distinguishable from those of the normal or non-modulated genes by an amount that exceeds background using clinical laboratory instrumentation.

Statistical values can be used to confidently distinguish modulated from non-modulated mRNA/miRNA and noise. Statistical tests find the mRNA/miRNA most significantly different between diverse groups of samples. The Student's t-test is an example of a robust statistical test that can be used to find significant differences between two groups. The lower the p-value, the more compelling the evidence that the gene shows a difference between the different groups. Nevertheless, since microarrays measure more than one mRNA/miRNA at a time, tens of thousands of statistical tests may be performed at one time. Because of this, one is unlikely to see small p-values just by chance and adjustments for this using a Sidak correction as well as a randomization/permutation experiment can be made. A p-value less than 0.05 by the t-test is evidence that the gene is significantly different. More compelling evidence is a p-value less then 0.05 after the Sidak correction is factored in. For a large number of samples in each group, a p-value less than 0.05 after the randomization/permutation test is the most compelling evidence of a significant difference.

In one embodiment, a method of generating a posterior probability score to enable diagnostic, prognostic, therapy-related, or physiological state specific biosignature scores can be arrived at by obtaining circulating biomarker data from a statistically significant number of patients; applying linear discrimination analysis to the data to obtain selected biomarkers; and applying weighted expression levels to the selected biomarkers with discriminate function factor to obtain a prediction model that can be applied as a posterior probability score. Other analytical tools can also be used to answer the same question such as, logistic regression and neural network approaches.

For instance, the following can be used for linear discriminant analysis:

where,

-   -   I(p_(s)i_(d))=The log base 2 intensity of the probe set enclosed         in parenthesis. d(cp)=The discriminant function for the disease         positive class d(C_(N))=The discriminant function for the         disease negative class     -   P(_(CP))=The posterior p-value for the disease positive class     -   P(_(CN))=The posterior p-value for the disease negative class

Numerous other well-known methods of pattern recognition are available. The following references provide some examples: Weighted Voting: Golub et al. (1999); Support Vector Machines: Su et al. (2001); and Ramaswamy et al. (2001); K-nearest Neighbors: Ramaswamy (2001); and Correlation Coefficients: van't Veer et al. (2002), all of which are herein incorporated by reference in their entireties.

A biosignature portfolio, further described below, can be established such that the combination of biomarkers in the portfolio exhibit improved sensitivity and specificity relative to individual biomarkers or randomly selected combinations of biomarkers. In one embodiment, the sensitivity of the biosignature portfolio can be reflected in the fold differences, for example, exhibited by a transcript's expression in the diseased state relative to the normal state. Specificity can be reflected in statistical measurements of the correlation of the signaling of transcript expression with the condition of interest. For example, standard deviation can be a used as such a measurement. In considering a group of biomarkers for inclusion in a biosignature portfolio, a small standard deviation in expression measurements correlates with greater specificity. Other measurements of variation such as correlation coefficients can also be used in this capacity.

Another parameter that can be used to select mRNA/miRNA that generate a signal that is greater than that of the non-modulated mRNA/miRNA or noise is the use of a measurement of absolute signal difference. The signal generated by the modulated mRNA/miRNA expression is at least 20% different than those of the normal or non-modulated gene (on an absolute basis). It is even more preferred that such mRNA/miRNA produce expression patterns that are at least 30% different than those of normal or non-modulated mRNA/miRNA.

MiRNA can also be detected and measured by amplification from a biological sample and measured using methods described in U.S. Pat. No. 7,250,496, U.S. Application Publication Nos. 20070292878, 20070042380 or 20050222399 and references cited therein, each of which is herein incorporated by reference in its entirety. The microRNA can be assessed as in U.S. Pat. No. 7,888,035, entitled “METHODS FOR ASSESSING RNA PATTERNS,” issued Feb. 15, 2011, which application is incorporated by reference herein in its entirety.

Peptide nucleic acids (PNAs) which are a new class of synthetic nucleic acid analogs in which the phosphate-sugar polynucleotide backbone is replaced by a flexible pseudo-peptide polymer may be utilized in analysis of a biosignature. PNAs are capable of hybridizing with high affinity and specificity to complementary RNA and DNA sequences and are highly resistant to degradation by nucleases and proteinases. Peptide nucleic acids (PNAs) are an attractive new class of probes with applications in cytogenetics for the rapid in situ identification of human chromosomes and the detection of copy number variation (CNV). Multicolor peptide nucleic acid-fluorescence in situ hybridization (PNA-FISH) protocols have been described for the identification of several human CNV-related disorders and infectious diseases. PNAs can also be utilized as molecular diagnostic tools to non-invasively measure oncogene mRNAs with tumor targeted radionuclide-PNA-peptide chimeras. Methods of using PNAs are described further in Pellestor F et al, Curr Pharm Des. 2008; 14(24):2439-44, Tian X et al, Ann N Y Acad Sci. 2005 November; 1059:106-44, Paulasova P and Pellestor F, Annales de Génétique, 47 (2004) 349-358, Stender H Expert Rev Mol Diagn. 2003 September; 3(5):649-55. Review, Vigneault et al., Nature Methods, 5(9), 777-779 (2008), each reference is herein incorporated by reference in its entirety. These methods can be used to screen the genetic materials isolated from a vesicle. When applying these techniques to a cell-of-origin specific vesicle, they can be used to identify a given molecular signal that directly pertains to the cell of origin.

Mutational analysis may be carried out for mRNAs and DNA, including those that are identified from a vesicle. For mutational analysis of a target or biomarker that is of RNA origin, the RNA (mRNA, miRNA or other) can be reverse transcribed into cDNA and subsequently sequenced or assayed, such as for known SNPs (by Taqman SNP assays, for example) or single nucleotide mutations, as well as using sequencing to look for insertions or deletions to determine mutations present in the cell-of-origin. Multiplexed ligation dependent probe amplification (MLPA) could alternatively be used for the purpose of identifying CNV in small and specific areas of interest. For example, once the total RNA has been obtained from isolated colon cancer-specific vesicles, cDNA can be synthesized and primers specific for exons 2 and 3 of the KRAS gene can be used to amplify these two exons containing codons 12, 13 and 61 of the KRAS gene. The same primers used for PCR amplification can be used for Big Dye Terminator sequence analysis on the ABI 3730 to identify mutations in exons 2 and 3 of KRAS. Mutations in these codons are known to confer resistance to drugs such as Cetuximab and Panitumimab. Methods of conducting mutational analysis are described in Maheswaran S et al, Jul. 2, 2008 (10.1056/NEJMoa0800668) and Orita, M et al, PNAS 1989, (86): 2766-70, each of which is herein incorporated by reference in its entirety.

Other methods of conducting mutational analysis include miRNA sequencing. Applications for identifying and profiling miRNAs can be done by cloning techniques and the use of capillary DNA sequencing or “next-generation” sequencing technologies. The new sequencing technologies currently available allow the identification of low-abundance miRNAs or those exhibiting modest expression differences between samples, which may not be detected by hybridization-based methods. Such new sequencing technologies include the massively parallel signature sequencing (MPSS) methodology described in Nakano et al. 2006, Nucleic Acids Res. 2006; 34:D731-D735. doi: 10.1093/nar/gkj077, the Roche/454 platform described in Margulies et al. 2005, Nature. 2005; 437:376-380 or the Illumina sequencing platform described in Berezikov et al. Nat. Genet. 2006b; 38:1375-1377, each of which is incorporated by reference in its entirety.

Additional methods to determine a biosignature includes assaying a biomarker by allele-specific PCR, which includes specific primers to amplify and discriminate between two alleles of a gene simultaneously, single-strand conformation polymorphism (SSCP), which involves the electrophoretic separation of single-stranded nucleic acids based on subtle differences in sequence, and DNA and RNA aptamers. DNA and RNA aptamers are short oligonucleotide sequences that can be selected from random pools based on their ability to bind a particular molecule with high affinity. Methods of using aptamers are described in Ulrich H et al, Comb Chem High Throughput Screen. 2006 September; 9(8):619-32, Ferreira C S et al, Anal Bioanal Chem. 2008 February; 390(4):1039-50, Ferreira C S et al, Tumour Biol. 2006; 27(6):289-301, each of which is herein incorporated by reference in its entirety.

Biomarkers can also be detected using fluorescence in situ hybridization (FISH). Methods of using FISH to detect and localize specific DNA sequences, localize specific mRNAs within tissue samples or identify chromosomal abnormalities are described in Shaffer D R et al, Clin Cancer Res. 2007 Apr. 1; 13(7):2023-9, Cappuzo F et al, Journal of Thoracic Oncology, Volume 2, Number 5, May 2007, Moroni M et al, Lancet Oncol. 2005 May; 6(5):279-86, each of which is herein incorporated by reference in its entirety.

An illustrative schematic for analyzing a population of vesicles for their payload is presented in FIG. 63E. In an embodiment, the methods of the invention include characterizing a phenotype by capturing vesicles (6330) and determining a level of microRNA species contained therein (6331), thereby characterizing the phenotype (6332).

A biosignature comprising a circulating biomarker or vesicle can comprise a binding agent thereto. The binding agent can be a DNA, RNA, aptamer, monoclonal antibody, polyclonal antibody, Fabs, Fab′, single chain antibody, synthetic antibody, aptamer (DNA/RNA), peptoid, zDNA, peptide nucleic acid (PNA), locked nucleic acid (LNA), lectin, synthetic or naturally occurring chemical compounds (including but not limited to drugs and labeling reagents).

A binding agent can used to isolate or detect a vesicle by binding to a component of the vesicle, as described above. The binding agent can be used to detect a vesicle, such as for detecting a cell-of-origin specific vesicle. A binding agent or multiple binding agents can themselves form a binding agent profile that provides a biosignature for a vesicle. One or more binding agents can be selected from FIG. 2. For example, if a vesicle population is detected or isolated using two, three or four binding agents in a differential detection or isolation of a vesicle from a heterogeneous population of vesicles, the particular binding agent profile for the vesicle population provides a biosignature for the particular vesicle population. Numerous vesicle antigens that can be used as the targets of binding agents are described herein.

As an illustrative example, a vesicle for characterizing a cancer can be detected with one or more binding agents including, but not limited to, PSA, PSMA, PCSA, PSCA, B7H3, EpCam, TMPRSS2, mAB 5D4, XPSM-A9, XPSM-A10, Galectin-3, E-selectin, Galectin-1, or E4 (IgG2a kappa), or any combination thereof.

The binding agent can also be for a general vesicle biomarker, such as a “housekeeping protein” or antigen. The biomarker can be CD9, CD63, or CD81. For example, the binding agent can be an antibody for CD9, CD63, or CD81. The binding agent can also be for other proteins, such as for tissue specific or cancer specific vesicles. The binding agent can be for PCSA, PSMA, EpCam, B7H3, or STEAP. The binding agent can be for DR3, STEAP, epha2, TMEM211, MFG-E8, Annexin V, TF, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, or TETS. For example, the binding agent can be an antibody or aptamer for PCSA, PSMA, EpCam, B7H3, DR3, STEAP, epha2, TMEM211, MFG-E8, Annexin V, TF, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, or TETS.

Various proteins are not typically distributed evenly or uniformly on a vesicle shell. See, e.g., FIG. 64, which illustrates a schematic of protein expression patterns. Vesicle-specific proteins are typically more common, while cancer-specific proteins are less common. In some embodiments, capture of a vesicle is accomplished using a more common, less cancer-specific protein, such as one or more housekeeping proteins or antigen or general vesicle antigen (e.g., a tetraspanin), and one or more cancer-specific biomarkers and/or one or more cell-of-origin specific biomarkers is used in the detection phase. In another embodiment, one or more cancer-specific biomarkers and/or one or more cell-of-origin specific biomarkers are used for capture, and one or more housekeeping proteins or antigen or general vesicle antigen (e.g., a tetraspanin) is used for detection. In embodiments, the same biomarker is used for both capture and detection. Different binding agents for the same biomarker can be used, such as antibodies or aptamers that bind different epitopes of an antigen.

Additional cellular binding partners or binding agents may be identified by any conventional methods known in the art, or as described herein, and may additionally be used as a diagnostic, prognostic or therapy-related marker.

As an illustrative example, a vesicle for analysis for lung cancer can be detected with one or more binding agents including, but not limited to, SCLC specific aptamer HCA 12, SCLC specific aptamer HCC03, SCLC specific aptamer HCH07, SCLC specific aptamer HCH01, A-p50 aptamer (NF-KB), Cetuximab, Panitumumab, Bevacizumab, L19 Ab, F16 Ab, anti-CD45 (anti-ICAM-1, aka UV3), or L2G7 Ab (anti-HGF), or any combination thereof. In some embodiments, a binding agent for a lung cancer vesicle comprises a binding agent to one or more of SPB, SPC, PSP9.5, NDUFB7, gal3-b2c10, iC3b, MUC1, GPCR, CABYR and muc17.

A vesicle for characterizing colon cancer can be detected with one or more binding agents including, but not limited to, angiopoietin 2 specific aptamer, beta-catenin aptamer, TCF1 aptamer, anti-Derlin1 ab, anti-RAGE, mAbgb3.1, Galectin-3, Cetuximab, Panitumumab, Matuzumab, Bevacizumab, or Mac-2, or any combination thereof.

A vesicle for characterizing adenoma versus colorectal cancer (CRC) can be detected with one or more binding agents including, but not limited to, Complement C3, histidine-rich glycoprotein, kininogen-1, or Galectin-3, or any combination thereof.

A vesicle for characterizing adenoma with low grade hyperplasia versus adenoma with high grade hyperplasia can be detected with a binding agent such as, but not limited to, Galectin-3 or any combination of binding agents specific for this comparison.

A vesicle for characterizing CRC versus normal state can be detected with one or more binding agents including, but not limited to, anti-ODC mAb, anti-CEA mAb, or Mac-2, or any combination thereof.

A vesicle for characterizing prostate cancer can be detected with one or more binding agents including, but not limited to, PSA, PSMA, TMPRSS2, mAB 5D4, XPSM-A9, XPSM-A10, Galectin-3, E-selectin, Galectin-1, or E4 (IgG2a kappa), or any combination thereof.

A vesicle for characterizing melanoma can be detected with one or more binding agents including, but not limited to, Tremelimumab (anti-CTLA4), Ipilimumumab (anti-CTLA4), CTLA-4 aptamers, STAT-3 peptide aptamers, Galectin-1, Galectin-3, or PNA, or any combination thereof.

A vesicle for characterizing pancreatic cancer can be detected with one or more binding agents including, but not limited to, H38-15 (anti-HGF) aptamer, H38-21(anti-HGF) aptamer, Matuzumab, Cetuximanb, or Bevacizumab, or any combination thereof.

A vesicle for characterizing brain cancer can be detected with one or more binding agents including, but not limited to, aptamer III.1 (pigpen) and/or TTA1 (Tenascin-C) aptamer, or any combination thereof.

A vesicle for characterizing psoriasis can be detected with one or more binding agents including, but not limited to, E-selectin, ICAM-1, VLA-4, VCAM-1, alphaEbeta7, or any combination thereof.

A vesicle for characterizing cardiovascular disease (CVD) can be detected with one or more binding agents including, but not limited to, RB007 (factor IXA aptamer), ARC1779 (anti VWF) aptamer, or LOX1, or any combination thereof.

A vesicle for characterizing hematological malignancies can be detected with one or more binding agents including, but not limited to, anti-CD20 and/or anti-CD52, or any combination thereof.

A vesicle for characterizing B-cell chronic lymphocytic leukemias can be detected with one or more binding agents including, but not limited to, Rituximab, Alemtuzumab, Apt48 (BCL6), RO-60, or D-R15-8, or any combination thereof.

A vesicle for characterizing B-cell lymphoma can be detected with one or more binding agents including, but not limited to, Ibritumomab, Tositumomab, Anti-CD20 Antibodies, Alemtuzumab, Galiximab, Anti-CD40 Antibodies, Epratuzumab, Lumiliximab, Hu1D10, Galectin-3, or Apt48, or any combination thereof.

A vesicle for characterizing Burkitt's lymphoma can be detected with one or more binding agents including, but not limited to, TD05 aptamer, IgM mAB (38-13), or any combination thereof.

A vesicle for characterizing cervical cancer can be detected with one or more binding agents including, but not limited to, Galectin-9 and/or HPVE7 aptamer, or any combination thereof.

A vesicle for characterizing endometrial cancer can be detected with one or more binding agents including, but not limited to, Galectin-1 or any combinations of binding agents specific for endometrial cancer.

A vesicle for characterizing head and neck cancer can be detected with one or more binding agents including, but not limited to, (111)In-cMAb U36, anti-LOXL4, U36, BIWA-1, BIWA-2, BIWA-4, or BIWA-8, or any combination thereof.

A vesicle for characterizing IBD can be detected with one or more binding agents including, but not limited to, ACCA (anti-glycan Ab), ALCA (anti-glycan Ab), or AMCA (anti-glycan Ab), or any combination thereof.

A vesicle for characterizing diabetes can be detected with one or more binding agents including, but not limited to, RBP4 aptamer or any combination of binding agents specific for diabetes.

A vesicle for characterizing fibromyalgia can be detected with one or more binding agents including, but not limited to, L-selectin or any combination of binding agents specific for fibromyalgia.

A vesicle for characterizing multiple sclerosis (MS) can be detected with one or more binding agents including, but not limited to, Natalizumab (Tysabri) or any combination of binding agents specific for MS.

In addition, a vesicle for characterizing rheumatic disease can be detected with one or more binding agents including, but not limited to, Rituximab (anti-CD20 Ab) and/or Keliximab (anti-CD4 Ab), or any combination of binding agents specific for rheumatic disease.

A vesicle for characterizing Alzheimer disease can be detected with one or more binding agents including, but not limited to, TH14-BACE1 aptapers, 510-BACE1 aptapers, anti-Abeta, Bapineuzumab (AAB-001)-Elan, LY2062430 (anti-amyloid beta Ab)-Eli Lilly, or BACE1-Anti sense, or any combination thereof.

A vesicle for characterizing Prion specific diseases can be detected with one or more binding agents including, but not limited to, rhuPrP(c) aptamer, DP7 aptamer, Thioaptamer 97, SAF-93 aptamer, 15B3 (anti-PrPSc Ab), monoclonal anti PrPSc antibody P1:1, 1.5D7, 1.6F4 Abs, mab 14D3, mab 4F2, mab 8G8, or mab 12F10, or any combination thereof.

A vesicle for characterizing sepsis can be detected with one or more binding agents including, but not limited to, HA-1A mAb, E-5 mAb, TNF-alpha MAb, Afelimomab, or E-selectin, or any combination thereof.

A vesicle for characterizing schizophrenia can be detected with one or more binding agents including, but not limited to, L-selectin and/or N-CAM, or any combination of binding agents specific for schizophrenia.

A vesicle for characterizing depression can be detected with one or more binding agents including, but not limited to, GPIb or any combination of binding agents specific for depression.

A vesicle for characterizing GIST can be detected with one or more binding agents including, but not limited to, ANTI-DOG1 Ab or any combination of binding agents specific for GIST.

A vesicle for characterizing esophageal cancer can be detected with one or more binding agents including, but not limited to, CaSR binding agent or any combination of binding agents specific for esophageal cancer.

A vesicle for characterizing gastric cancer can be detected with one or more binding agents including, but not limited to, Calpain nCL-2 binding agent and/or drebrin binding agent, or any combination of binding agents specific for gastric cancer.

A vesicle for characterizing COPD can be detected with one or more binding agents including, but not limited to, CXCR3 binding agent, CCR5 binding agent, or CXCR6 binding agent, or any combination of binding agents specific for COPD.

A vesicle for characterizing asthma can be detected with one or more binding agents including, but not limited to, VIP binding agent, PACAP binding agent, CGRP binding agent, NT3 binding agent, YKL-40 binding agent, S-nitrosothiols, SCCA2 binding agent, PAI binding agent, amphiregulin binding agent, or Periostin binding agent, or any combination of binding agents specific for asthma.

A vesicle for characterizing vulnerable plaque can be detected with one or more binding agents including, but not limited to, Gd-DTPA-g-mimRGD (Alpha v Beta 3 integrin binding peptide), or MMP-9 binding agent, or any combination of binding agents specific for vulnerable plaque.

A vesicle for characterizing ovarian cancer can be detected with one or more binding agents including, but not limited to, (90) Y-muHMFG1 binding agent and/or OC125 (anti-CA125 antibody), or any combination of binding agents specific for ovarian cancer.

The binding agent can also be for a general vesicle biomarker, such as a “housekeeping protein” or antigen. The biomarker can be CD9, CD63, or CD81. For example, the binding agent can be an antibody for CD9, CD63, or CD81. The binding agent can also be for other proteins, such as for prostate specific or cancer specific vesicles. The binding agent can be for PCSA, PSMA, EpCam, B7H3, or STEAP. For example, the binding agent can be an antibody for PCSA, PSMA, EpCam, B7H3, or STEAP.

Various proteins may not be distributed evenly or uniformly on a vesicle shell. See, e.g., FIG. 64, which illustrates a schematic of protein expression patterns. Vesicle-specific proteins are typically more common, while cancer-specific proteins are less common. In some embodiments, capture of a vesicle is accomplished using a more common, less cancer-specific protein, such as a housekeeping protein or antigen, and cancer-specific proteins is used in the detection phase.

Furthermore, additional cellular binding partners or binding agents may be identified by any conventional methods known in the art, or as described herein, and may additionally be used as a diagnostic, prognostic or therapy-related marker.

Biosignatures for Cancers

As described herein, biosignatures comprising circulating biomarkers can be used to characterize a cancer. This Section presents a non-exclusive list of biomarkers that can be used as part of a biosignature, e.g., for prostate, GI, or ovarian cancer. In some embodiments, the circulating biomarkers are associated with a vesicle or with a population of vesicles. For example, circulating biomarkers associated with vesicles can be used to capture and/or to detect a vesicle or a vesicle population. This Section presents a non-exclusive list of biomarkers that can be used as part of a biosignature, e.g., for prostate, GI, or ovarian cancer.

It will be appreciated that the biomarkers presented herein may be useful in biosignatures for other diseases, e.g., other proliferative disorders and cancers of other cellular or tissue origins. For example, transformation in various cell types can be due to common events, e.g., mutation in p53 or other tumor suppressor. A biosignature comprising cell-of-origin biomarkers and cancer biomarkers can be used to further assess the nature of the cancer. Biomarkers for metastatic cancer may be used with cell-of-origin biomarkers to assess a metastatic cancer. Such biomarkers for use with the invention include those in Dawood, Novel biomarkers of metastatic cancer, Exp Rev Mol Diag July 2010, Vol. 10, No. 5, Pages 581-590, which publication is incorporated herein by reference in its entirety. A biosignature for a cancer can comprise one or more known cancer marker, such as those described herein or known in the art.

The biosignatures of the invention may comprise markers that are upregulated, downregulated, or have no change, depending on the reference. Solely for illustration, if the reference is a normal sample, the biosignature may indicate that the subject is normal if the subject's biosignature is not changed compared to the reference. Alternately, the biosignature may comprise a mutated nucleic acid or amino acid sequence so that the levels of the components in the biosignature are the same between a normal reference and a diseased sample. In another case, the reference can be a cancer sample, such that the subject's biosignature indicates cancer if the subject's biosignature is substantially similar to the reference. The biosignature of the subject can comprise components that are both upregulated and downregulated compared to the reference. Solely for illustration, if the reference is a normal sample, a cancer biosignature can comprise both upregulated oncogenes and downregulated tumor suppressors. Vesicle markers can also be differentially expressed in various settings. For example, tetraspanins may be overexpressed in cancer vesicles compared to non-cancer vesicles, whereas MFG-E8 can be overexpressed in non-cancer vesicles as compared to cancer vesicles.

The biosignature for characterizing a cancer can include one or more known cancer gene. In an embodiment, the one or more known cancer gene is selected from the group consisting of ABL1, ABL2, ACSL3, AF15Q14, AF1Q, AF3p21, AF5q31, AKAP9, AKT1, AKT2, ALDH2, ALK, ALO17, APC, ARHGEF12, ARHH, ARID1A, ARID2, ARNT, ASPSCR1, ASXL1, ATF1, ATIC, ATM, ATRX, BAP1, BCL10, BCL11A, BCL11B, BCL2, BCL3, BCL5, BCL6, BCL7A, BCL9, BCOR, BCR, BHD, BIRC3, BLM, BMPR1A, BRAF, BRCA1, BRCA2, BRD3, BRD4, BRIP1, BTG1, BUB1B, C12 or 19, C15orf21, C15orf55, C16orf75, CANT1, CARD11, CARS, CBFA2T1, CBFA2T3, CBFB, CBL, CBLB, CBLC, CCNB11P1, CCND1, CCND2, CCND3, CCNE1, CD273, CD274, CD74, CD79A, CD79B, CDH1, CDH11, CDK12, CDK4, CDK6, CDKN2A, CDKN2a(p14), CDKN2C, CDX2, CEBPA, CEP1, CHCHD7, CHEK2, CHIC2, CHN1, CIC, CIITA, CLTC, CLTCL1, CMKOR1, COL1A1, COPEB, COX6C, CREB1, CREB3L1, CREB3L2, CREBBP, CRLF2, CRTC3, CTNNB1, CYLD, D105170, DAXX, DDB2, DDIT3, DDX10, DDX5, DDX6, DEK, DICER1, DNMT3A, DUX4, EBF1, EGFR, EIF4A2, ELF4, ELK4, ELKS, ELL, ELN, EML4, EP300, EPS15, ERBB2, ERCC2, ERCC3, ERCC4, ERCC5, ERG, ETV1, ETV4, ETV5, ETV6, EVI1, EWSR1, EXT1, EXT2, EZH2, FACL6, FAM22A, FAM22B, FAM46C, FANCA, FANCC, FANCD2, FANCE, FANCF, FANCG, FBXO11, FBXW7, FCGR2B, FEV, FGFR1, FGFR1OP, FGFR2, FGFR3, FH, FHIT, FIP1L1, FLI1, FLJ27352, FLT3, FNBP1, FOXL2, FOXO1A, FOXO3A, FOXP1, FSTL3, FUBP1, FUS, FVT1, GAS7, GATA1, GATA2, GATA3, GMPS, GNA11, GNAQ, GNAS, GOLGA5, GOPC, GPC3, GPHN, GRAF, HCMOGT-1, HEAB, HERPUD1, HEY1, HIP1, HIST1H4I, HLF, HLXB9, HMGA1, HMGA2, HNRNPA2B1, HOOK3, HOXA11, HOXA13, HOXA9, HOXC11, HOXC13, HOXD11, HOXD13, HRAS, HRPT2, HSPCA, HSPCB, IDH1, IDH2, IGH@, IGK@, IGL@, IKZF1, IL2, IL21R, IL6ST, IL7R, IRF4, IRTA1, ITK, JAK1, JAK2, JAK3, JAZF1, JUN, KDM5A, KDM5C, KDM6A, KDR, KIAA1549, KIT, KLK2, KRAS, KTN1, LAF4, LASP1, LCK, LCP1, LCX, LHFP, LIFR, LMO1, LMO2, LPP, LYL1, MADH4, MAF, MAFB, MALT1, MAML2, MAP2K4, MDM2, MDM4, MDS1, MDS2, MECT1, MED12, MEN1, MET, MITF, MKL1, MLF1, MLH1, MLL, MLL2, MLL3, MLLT1, MLLT10, MLLT2, MLLT3, MLLT4, MLLT6, MLLT7, MN1, MPL, MSF, MSH2, MSH6, MSI2, MSN, MTCP1, MUC1, MUTYH, MYB, MYC, MYCL1, MYCN, MYD88, MYH11, MYH9, MYST4, NACA, NBS1, NCOA1, NCOA2, NCOA4, NDRG1, NF1, NF2, NFE2L2, NFIB, NFKB2, NIN, NKX2-1, NONO, NOTCH1, NOTCH2, NPM1, NR4A3, NRAS, NSD1, NTRK1, NTRK3, NUMA1, NUP214, NUP98, OLIG2, OMD, P2RY8, PAFAH1B2, PALB2, PAX3, PAX5, PAX7, PAX8, PBRM1, PBX1, PCM1, PCSK7, PDE4DIP, PDGFB, PDGFRA, PDGFRB, PER1, PHOX2B, PICALM, PIK3CA, PIK3R1, PIM1, PLAG1, PML, PMS1, PMS2, PMX1, PNUTL1, POU2AF1, POU5F1, PPARG, PPP2R1A, PRCC, PRDM1, PRDM16, PRF1, PRKAR1A, PRO1073, PSIP2, PTCH, PTEN, PTPN11, RAB5EP, RAD51L1, RAF1, RALGDS, RANBP17, RAP1GDS1, RARA, RB1, RBM15, RECQL4, REL, RET, ROS1, RPL22, RPN1, RUNDC2A, RUNX1, RUNXBP2, SBDS, SDH5, SDHB, SDHC, SDHD, SEPT6, SET, SETD2, SF3B1, SFPQ, SFRS3, SH3GL1, SIL, SLC45A3, SMARCA4, SMARCB1, SMO, SOCS1, SOX2, SRGAP3, SRSF2, SS18, SS18L1, SSH3BP1, SSX1, SSX2, SSX4, STK11, STL, SUFU, SUZ12, SYK, TAF15, TAL1, TAL2, TCEA1, TCF1, TCF12, TCF3, TCF7L2, TCL1A, TCL6, TET2, TFE3, TFEB, TFG, TFPT, TFRC, THRAP3, TIF1, TLX1, TLX3, TMPRSS2, TNFAIP3, TNFRSF14, TNFRSF17, TNFRSF6, TOP1, TP53, TPM3, TPM4, TPR, TRA@, TRB@, TRD@, TRIM27, TRIM33, TRIP11, TSC1, TSC2, TSHR, TTL, U2AF1, USP6, VHL, VTI1A, WAS, WHSC1, WHSC1L1, WIF1, WRN, WT1, WTX, XPA, XPC, XPO1, YWHAE, ZNF145, ZNF198, ZNF278, ZNF331, ZNF384, ZNF521, ZNF9, ZRSR2, and a combination thereof. In another embodiment, the one or more known cancer gene is selected from the group consisting of AR, androgen receptor; ARPC1A, actin-related protein complex 2/3 subunit A; AURKA, Aurora kinase A; BAG4, BCl-2 associated anthogene 4; BCl212, BCl-2 like 2; BIRC2, Baculovirus IAP repeat containing protein 2; CACNA1E, calcium channel voltage dependent alpha-1E subunit; CCNE1, cyclin E1; CDK4, cyclin dependent kinase 4; CHD1L, chromodomain helicase DNA binding domain 1-like; CKS1B, CDC28 protein kinase 1B; COPS3, COP9 subunit 3; DCUN1D1, DCN1 domain containing protein 1; DYRK2, dual specificity tyrosine phosphorylation regulated kinase 2; EEF1A2, eukaryotic elongation transcription factor 1 alpha 2; EGFR, epidermal growth factor receptor; FADD, Fas-associated via death domain; FGFR1, fibroblast growth factor receptor 1, GATA6, GATA binding protein 6; GPC5, glypican 5; GRB7, growth factor receptor bound protein 7; MAP3K5, mitogen activated protein kinase kinase kinase 5; MED29, mediator complex subunit 5; MITF, microphthalmia associated transcription factor; MTDH, metadherin; NCOA3, nuclear receptor coactivator 3; NKX2-1, NK2 homeobox 1; PAK1, p21/CDC42/RAC1-activated kinase 1; PAX9, paired box gene 9; PIK3CA, phosphatidylinositol-3 kinase catalytic a; PLA2G10, phopholipase A2, group X; PPM1D, protein phosphatase magnesium-dependent 1D; PTK6, protein tyrosine kinase 6; PRKCI, protein kinase C iota; RPS6 KB1, ribosomal protein s6 kinase 70 kDa; SKP2, s-phase kinase associated protein; SMURF1, sMAD specific E3 ubiquitin protein ligase 1; SHH, sonic hedgehog homologue; STARD3, sTAR-related lipid transfer domain containing protein 3; YWHAQ, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta isoform; ZNF217, zinc finger protein 217, and a combination thereof. In still another embodiment, the one or more known cancer gene is a mitosis related gene selected from the group consisting of Aurora kinase A (AURKA); Aurora kinase B (AURKB); Baculoviral IAP repeat-containing 5, survivin (BIRC5); Budding uninhibited by benzimidazoles 1 homolog (BUB1); Budding uninhibited by benzimidazoles 1 homolog beta, BUBR1 (BUB1B); Budding uninhibited by benzimidazoles 3 homolog (BUB3); CDC28 protein kinase regulatory subunit 1B (CKS1B); CDC28 protein kinase regulatory subunit 2 (CKS2); Cell division cycle 2 (CDC2)/CDK1 Cell division cycle 20 homolog (CDC20); Cell division cycle-associated 8, borealin (CDCA8); Centromere protein F, mitosin (CENPF); Centrosomal protein 110 kDa (CEP110); Checkpoint with forkhead and ring finger domains (CHFR); Cyclin B1 (CCNB1); Cyclin B2 (CCNB2); Cytoskeleton-associated protein 5 (CKAP5/ch-TOG); Microtubule-associated protein RP/EB family member 1. End-binding protein 1, EB1 (MAPRE1); Epithelial cell transforming sequence 2 oncogene (ECT2); Extra spindle poles like 1, separase (ESPL1); Forkhead box M1 (FOXM1); H2A histone family, member X (H2AFX); Kinesin family member 4A (KIF4A); Kinetochore-associated 1 (KNTC1/ROD); Kinetochore-associated 2; highly expressed in cancer 1 (KNTC2/HEC1); Large tumor suppressor, homolog 1 (LATS1); Large tumor suppressor, homolog 2 (LATS2); Mitotic arrest deficient-like 1; MAD1 (MAD1L1); Mitotic arrest deficient-like 2; MAD2 (MAD2L1); Mps1 protein kinase (TTK); Never in mitosis gene a-related kinase 2 (NEK2); Ninein, GSK3b interacting protein (NIN); Non-SMC condensin I complex, subunit D2 (NCAPD2/CNAP1); Non-SMC condensin I complex, subunit H(NACPH/CAPH); Nuclear mitotic apparatus protein 1 (NUMA1); Nucleophosmin (nucleolar phosphoprotein B23, numatrin); (NPM1); Nucleoporin (NUP98); Pericentriolar material 1 (PCM1); Pituitary tumor-transforming 1, securin (PTTG1); Polo-like kinase 1 (PLK1); Polo-like kinase 4 (PLK4/SAK); Protein (peptidylprolyl cis/trans isomerase) NIMA-interacting 1 (PIN1); Protein regulator of cytokinesis 1 (PRC1); RAD21 homolog (RAD21); Ras association (RalGDS/AF-6); domain family 1 (RASSF1); Stromal antigen 1 (STAG1); Synuclein-c, breast cancer-specific protein 1 (SNCG, BCSG1); Targeting protein for Xklp2 (TPX2); Transforming, acidic coiled-coil containing protein 3 (TACC3); Ubiquitin-conjugating enzyme E2C (UBE2C); Ubiquitin-conjugating enzyme E2I (UBE2I/UBC9); ZW10 interactor, (ZWINT); ZW10, kinetochore-associated homolog (ZW10); Zwilch, kinetochore-associated homolog (ZWILCH); and a combination thereof. For illustrative descriptions of known cancer genes, see, e.g., Futreal et al., A CENSUS OF HUMAN CANCER GENES, Nature Reviews Cancer, 4:177-183 (2004) and online supplemental data; Perez de Castro et al., A census of mitotic cancer genes: new insights into tumor cell biology and cancer therapy; Carcinogenesis vol. 28 no. 5 pp. 899-912, 2007; Santarius et al., A census of amplified and overexpressed human cancer genes, Nature Reviews Cancer, 10:59-64 (2010) and online supplemental data; each of which publication and supplemental data thereof is herein incorporated by reference in its entirety. The one or more known cancer gene can be a gene identified by the Cancer Gene Census project of the Wellcome Trust Sanger Institute, available online at www.sanger.ac.uk/genetics/CGP/Census/. The one or more known cancer gene can be a gene identified by the Amplified and Overexpressed Genes In Cancer project of The Institute of Cancer Research, available online at www.amplicon.icr.ac.uk/.

Prostate Cancer

Prostate-specific antigen (PSA) is a protein produced by the cells of the prostate gland. PSA is present in small quantities in the serum of normal men, and is often elevated in the presence of prostate cancer (PCa) and in other prostate disorders. A blood test to measure PSA is currently used for the screening of prostate cancer, but this effectiveness has also been questioned. For example, PSA levels can be increased by prostate infection, irritation, benign prostatic hyperplasia (BPH), digital rectal examination (DRE) and recent ejaculation, producing a false positive result that can lead to unnecessary prostate biopsy and concomitant morbidities. BPH is a common cause of elevated PSA levels. PSA may indicate whether there is something wrong with the prostate, but it cannot effectively differentiate between BPH and PCa. PCA3, a transcript found to be overexpressed by prostate cancer cells, is thought to be slightly more specific for PCa, but this depends on the cutoffs used for PSA and PCA3, as well as the populations studied.

The invention provides circulating biomarkers can be used to distinguish BPH and PCa. A biomarker panel is assessed to distinguish BPH from PCa. The panel can be used to detect vesicles displaying certain surface markers. In some embodiments, the surface markers comprise one or more of BCMA, CEACAM-1, HVEM, IL-1 R4, IL-10 Rb and Trappin-2. The levels of the biomarkers in vesicles derived from blood samples can be assayed and then used to distinguish BPH from PCa.

In another aspect, microRNAs (miRs) are used to differentiate between BPH and prostate cancer. The miRs can be isolated directly from a patient sample, and/or vesicles derived from patient samples can be analyzed for miR payload contained within the vesicles. The sample can be a bodily fluid, including semen, urine, blood, serum or plasma. The sample can also comprise a tissue or biopsy sample. A number of different methodologies are available for detecting miRs as described herein. In some embodiments, arrays of miR panels are use to simultaneously query the expression of multiple miRs. For example, the Exiqon mIRCURY LNA microRNA PCR system panel (Exiqon, Inc., Woburn, Mass.) can be used for such purposes. miRs that distinguish BPH and PCa can be overexpressed in BPH samples as compared to PCa samples, including without limitation one or more of: hsa-miR-329, hsa-miR-30a, hsa-miR-335, hsa-miR-152, hsa-miR-151-5p, hsa-miR-200a and hsa-miR-145. Alternately, miRs that distinguish BPH and PCa can be overexpressed in PCa samples versus BPH samples, including without limitation one or more of: hsa-miR-29a, hsa-miR-106b, hsa-miR-595, hsa-miR-142-5p, hsa-miR-99a, hsa-miR-20b, hsa-miR-373, hsa-miR-502-5p, hsa-miR-29b, hsa-miR-142-3p, hsa-miR-663, hsa-miR-423-5p, hsa-miR-15a, hsa-miR-888, hsa-miR-361-3p, hsa-miR-365, hsa-miR-10b, hsa-miR-199a-3p, hsa-miR-181a, hsa-miR-19a, hsa-miR-125b, hsa-miR-760, hsa-miR-7a, hsa-miR-671-5p, hsa-miR-7c, hsa-miR-1979, and hsa-miR-103.

The expression levels of one or more of the above miRs can be assessed and compared to reference levels to detect miRs that are differentially expressed, thereby providing a diagnostic, prognostic or theranostic readout. The reference levels can be those of the miRs in exosomes derived from normal patients, e.g., patients without prostate disease. Thus, differential expression of one or more miRs from the reference levels can indicate that the sample differs from normal, e.g., comprises BPH or PCa. The reference levels can be those of the miRs in exosomes derived from BPH patients. Thus, differential expression of one or more miRs from the reference levels can indicate that the sample differs from BPH, e.g., comprises normal or PCa. The reference levels can be those of the miRs in exosomes derived from PCa patients. Thus, differential expression of one or more miRs from the reference levels can indicate that the sample differs from PCa, e.g., comprises normal or BPH.

In some embodiments, the level of one or more miR in the test sample are correlated with the level of the same miRs in a reference sample, thereby providing a diagnostic, prognostic or theranostic readout. The reference sample can comprise the miR levels of one or more samples with BPH, PCa, or can be from normals without BPH or PCa. When the level of one or more miR in the test sample correlates most closely with that of the normal reference levels, the test sample can be classified as normal. When the level of one or more miR in the test sample correlates most closely with that of the BPH reference levels, the test sample can be classified as BPH. When the level of one or more miR in the test sample correlates most closely with that of the PCa reference levels, the test sample can be classified as PCa.

A biosignature can be used to characterize prostate cancer. As described above, a biosignature for prostate cancer can comprise a binding agent associated with prostate cancer (for example, as shown in FIG. 2), and one or more additional biomarkers, such as shown in FIG. 19. For example, a biosignature for prostate cancer can comprise a binding agent to PSA, PSMA, TMPRSS2, mAB 5D4, XPSM-A9, XPSM-A10, Galectin-3, E-selectin, Galectin-1, E4 (IgG2a kappa), or any combination thereof, with one or more additional biomarkers, such as one or more miRNA, one or more DNA, one or more additional peptide, protein, or antigen associated with prostate cancer, such as, but not limited to, those shown in FIG. 19.

A biosignature for prostate cancer can comprise an antigen associated with prostate cancer (for example, as shown in FIG. 1), and one or more additional biomarkers, such as shown in FIG. 19. A biosignature for prostate cancer can comprise one or more antigens associated with prostate cancer, such as, but not limited to, KIA1, intact fibronectin, PSA, EZH2 (Enhancer of zeste homolog 2), TMPRSS2, a TMPRSS2 fusion, FASLG, TNFSF10, PCSA, PSMA, NGEP, IL-7R1, CSCR4, CysLT1R, TRPM8, Kv1.3, TRPV6, TRPM8, PSGR, MISIIR, or any combination thereof. A biosignature for prostate cancer can also comprise one of more vesicle antigens selected from PSMA, PCSA, B7-H3, IL 6, OPG-13 (OPG), IL6R, PA2G4, EZH2, RUNX2, SERPINB3, or any combination thereof. The biosignature for prostate cancer can comprise one or more of the aforementioned antigens and one or more additional biomarkers, such as, but not limited to miRNA, mRNA, DNA, or any combination thereof.

A biosignature for prostate cancer can also comprise one or more antigens associated with prostate cancer, such as, but not limited to, KIA1, intact fibronectin, PSA, EZH2, PCA3, TMPRSS2, TMPRSS2-ERG, FASLG, TNFSF10, PSMA, PCSA, NGEP, IL-7R1, CSCR4, CysLT1R, TRPM8, Kv1.3, TRPV6, TRPM8, PSGR, MISIIR, B7-H3, IL 6, OPG-13 (OPG), IL6R, PA2G4, RUNX2, or any combination thereof, and one or more miRNA biomarkers, such as, but not limited to, miR-202, miR-210, miR-296, miR-320, miR-370, miR-373, miR-498, miR-503, miR-184, miR-198, miR-302c, miR-345, miR-491, miR-513, miR-32, miR-182, miR-31, miR-26a-1/2, miR-200c, miR-375, miR-196a-1/2, miR-370, miR-425, miR-425, miR-194-1/2, miR-181a-1/2, miR-34b, let-71, miR-188, miR-25, miR-106b, miR-449, miR-99b, miR-93, miR-92-1/2, miR-125a, miR-141, let-7a, let-7b, let-7c, let-7d, let-7g, miR-16, miR-23a, miR-23b, miR-26a, miR-92, miR-99a, miR-103, miR-125a, miR-125b, miR-143, miR-145, miR-195, miR-199, miR-221, miR-222, miR-497, let-7f, miR-19b, miR-22, miR-26b, miR-27a, miR-27b, miR-29a, miR-29b, miR-30_(—)5p, miR-30c, miR-100, miR-141, miR-148a, miR-205, miR-520h, miR-494, miR-490, miR-133a-1, miR-1-2, miR-218-2, miR-220, miR-128a, miR-221, miR-499, miR-329, miR-340, miR-345, miR-410, miR-126, miR-205, miR-7-1/2, miR-145, miR-34a, miR-487, miR-27b, miR-103, miR-146a, miR-22, miR-382, miR-23a, miR-376c, miR-335, miR-142-5p, miR-221, miR-142-3p, miR-151-3p, miR-21, let-7b, or any combination thereof.

A biosignature for prostate cancer can also comprise one or more circulating biomarkers, such as microRNAs associated with prostate cancer, including those described in Brase et al., Circulating miRNAs are correlated with tumor progression in prostate cancer. Int J Cancer. 2011 Feb. 1; 128(3):608-16; Wach et al., MiRNA profiles of prostate carcinoma detected by multi-platform miRNA screening. Int J Cancer. 2011 Mar. 11. doi: 10.1002/ijc.26064; Gordanpour et al., miR-221 Is Down-regulated in TMPRSS2:ERG Fusion-positive Prostate Cancer. Anticancer Res. 2011 February; 31(2):403-10; Hagman et al., miR-34c is downregulated in prostate cancer and exerts tumor suppressive functions. Int J Cancer. 2010 Dec. 15; 127(12):2768-76; Sun et al., miR-99 Family of MicroRNAs Suppresses the Expression of Prostate-Specific Antigen and Prostate Cancer Cell Proliferation. Cancer Res. 2011 Feb. 15; 71(4):1313-24; Bao et al., Polymorphisms inside MicroRNAs and MicroRNA Target Sites Predict Clinical Outcomes in Prostate Cancer Patients Receiving Androgen-Deprivation Therapy. Clin Cancer Res. 2011 Feb. 15; 17(4):928-936; Moltzahn et al., Microfluidic-based multiplex qRT-PCR identifies diagnostic and prognostic microRNA signatures in the sera of prostate cancer patients. Cancer Res. 2011 Jan. 15; 71(2):550-60; Carlsson et al., Validation of suitable endogenous control genes for expression studies of miRNA in prostate cancer tissues. Cancer Genet Cytogenet. 2010 Oct. 15; 202(2):71-75; Zhang et al., Serum miRNA-21: elevated levels in patients with metastatic hormone-refractory prostate cancer and potential predictive factor for the efficacy of docetaxel-based chemotherapy. Prostate. 2011 Feb. 15; 71(3):326-31; Majid et al., MicroRNA-205-directed transcriptional activation of tumor suppressor genes in prostate cancer. Cancer. 2010 Dec. 15; 116(24):5637-49; Kojima et al., MiR-34a attenuates paclitaxel-resistance of hormone-refractory prostate cancer PC3 cells through direct and indirect mechanisms. Prostate. 2010 Oct. 1; 70(14):1501-12; Lewinshtein et al., Genomic predictors of prostate cancer therapy outcomes. Expert Rev Mol Diagn. 2010 July; 10(5):619-36; each of which publication is hereby incorporated by reference in its entirety.

Furthermore, the miRNA for a prostate cancer biosignature can be a miRNA that interacts with PFKFB3, RHAMM (HMMR), cDNA FLJ42103, ASPM, CENPF, NCAPG, Androgen Receptor, EGFR, HSP90, SPARC, DNMT3B, GART, MGMT, SSTR3, TOP2B, or any combination thereof, such as those described herein and depicted in FIG. 60. The miRNA can also be miR-9, miR-629, miR-141, miR-671-3p, miR-491, miR-182, miR-125a-3p, miR-324-5p, miR-148B, miR-222, or any combination thereof.

The biosignature for prostate cancer can comprise one or more antigens associated with prostate cancer, such as, but not limited to, KIA1, intact fibronectin, PSA, EZH2, TMPRSS2, FASLG, TNFSF10, PSMA, PCSA, PSCA, NGEP, IL-7R1, CSCR4, CysLT1R, TRPM8, Kv1.3, TRPV6, TRPM8, PSGR, MISIIR, B7-H3, IL 6, OPG-13 (OPG), IL6R, PA2G4, RUNX2, or any combination thereof, and one or more additional biomarkers such as, but not limited to, the aforementioned miRNAs, mRNAs (such as, but not limited to, AR or PCA3), snoRNA (such as, but not limited to, U50) or any combination thereof.

The biosignature can also comprise one or more gene fusions, such as ACSL3-ETV1, C150RF21-ETV1, FLJ35294-ETV1, HERV-ETV1, TMPRSS2-ERG, TMPRSS2-ETV1/4/5, TMPRSS2-ETV4/5, SLC5A3-ERG, SLC5A3-ETV1, SLC5A3-ETV5 or KLK2-ETV4.

A vesicle can be isolated, assayed, or both, for one or more miRNA and one or more antigens associated with prostate cancer to provide a diagnostic, prognostic or theranostic profile, such as the stage of the cancer, the efficacy of the cancer, or other characteristics of the cancer. Alternatively, the vesicle can be directly assayed from a sample, such that the vesicle is not purified or concentrated prior to assaying for one or more miRNA or antigens associated with prostate cancer.

A biosignature for prostate cancer can be used to assess the efficacy of a therapy. For example, biomarkers that are elevated in PCa can be monitored before and after a treatment. A reduction in the level of the biomarker post-treatment can indicate that the treatment is efficacious. The same biosignature can be monitored overtime, e.g., to detect recurrence or relapse post-treatment. In some embodiments, the biosignature for monitoring treatment efficacy comprises monitoring vesicle associated microRNAs, including hsa-miR-27b, hsa-miR-103, hsa-miR-146a, hsa-miR-22, hsa-miR-382, hsa-miR-23a, hsa-miR-376c, hsa-miR-335, hsa-miR-142-5p, hsa-miR-221, hsa-miR-142-3p, hsa-miR-151-3p and hsa-miR-21, or any combination thereof.

As depicted in FIG. 68, a prostate cancer biosignature can comprise assaying EpCam, CD63, CD81, CD9, or any combination thereof, of a vesicle. The prostate cancer biosignature can comprise detection of EpCam, CD9, CD63, CD81, PCSA or any combination thereof. For example, the prostate cancer biosignature can comprise EpCam, CD9, CD63 and CD81 or PCSA, CD9, CD63 and CD81 (see for example, FIG. 70A). The prostate cancer biosignature can also comprise PCSA, PSMA, B7H3, or any combination thereof (see for example, FIG. 70B). In one embodiment, the biosignature comprises PMSA and one or more tetraspanins, e.g., CD9, CD63 and/or CD81. In another embodiment, the biosignature comprises PCSA and one or more tetraspanins, e.g., CD9, CD63 and/or CD81. In these embodiments, PMSA or PSCA can be used to capture vesicles and the one or more tetraspanins can be used for detection.

Furthermore, assessing a plurality of biomarkers can provide increased sensitivity, specificity, or signal intensity, as compared to assessing less than a plurality of biomarkers. For example, assessing PSMA and B7H3 can provide increased sensitivity in detection as compared to assessing PSMA or B7H3 alone. Assessing CD9 and CD63 can provide increased sensitivity in detection as compared to assessing CD9 or CD63 alone. In one embodiment, one or more of the following biomarkers are detected: EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP, and EGFR. In another embodiment, EpCam+, CK+, CD45− vesicles are detected. In another embodiment, one or more of IL 6, OPG-13 (OPG), IL6R, PA2G4, RUNX2 are detected.

The antigens can be detected in panel combinations determined to enhance sensitivity and specificity. In one embodiment, a panel of biomarkers comprises OPG, PA2G4, RUNX2, and SERPI. In another embodiment, a panel of biomarkers comprises: PCSA and B7H3. In still another embodiment, a panel of biomarkers comprises: PA2G4, RUNX2, and SERPI. In an embodiment, a panel of biomarkers comprises: OPG, RUNX2, and SERPI. In another embodiment, a panel of biomarkers comprises: OPG, PA2G4, and SERPI. In yet another embodiment, a panel of biomarkers comprises: OPG, PA2G4, and RUNX2. In another embodiment, a panel of biomarkers comprises: OPG, PA2G4, RUNX2, SERPI, PCSA, and B7H3.

Prostate cancer can also be characterized based on meeting at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 criteria. For example, a number of different criteria can be used: 1) if the amount of vesicles in a sample from a subject is higher than a reference value; 2) if the amount of prostate cell derived vesicles is higher than a reference value; and 3) if the amount of vesicles with one or more cancer specific biomarkers is higher than a reference value, the subject is diagnosed with prostate cancer. The method can further include a quality control measure.

In another embodiment, one or more biosignature of a vesicle is used for the diagnosis between normal prostate and prostate cancer, or between normal prostate, BPH and PCa. Any appropriate biomarker disclosed herein can be used to distinguish PCa. In some embodiments, one or more general capture agents to a biomarker (or capture biomarker, a biomarker that is detected or bound by a capture agent) can be used to capture one or more vesicles from a sample from a subject.

Prostate specific biomarkers can be used to identify prostate specific vesicles. Cancer biomarkers can be used to identify cancer specific vesicles. In some embodiments, one or more of CD9, CD81 and CD63 are used as capture biomarkers. In some embodiments, PCSA is used as a prostate biomarker. In some embodiments, the one or more cancer biomarkers comprise one or more of EpCam and B7H3. Additional biomarkers that can distinguish PCa from normal include ICAM1, EGFR, STEAP1 and PSCA.

In some embodiments, the method of identifying prostate cancer in a subject comprises: (a) capturing a population of vesicles in a sample from the subject using a capture agent; (b) determining a level of one or more cancer biomarkers in the population of vesicles; (c) determining a level of one or more prostate biomarkers in the population of vesicles; and (d) identifying the subject as having prostate cancer if the level of the one or more cancer biomarkers and the level of one or more prostate biomarkers meet a predetermined threshold value. In some embodiments, the capture agent comprises one or more binding agents for CD9, CD81 and CD63. In some embodiments, the one or more prostate biomarker comprises PCSA and/or PSMA. In some embodiments, the one or more cancer biomarkers comprise one or more of EpCam and B7H3. In other embodiments, binding agents to the one or more prostate and/or cancer biomarkers are used as capture agents and binding agents to the one or more general vesicle markers are used as detection agents. In some embodiments, the predetermined threshold value comprises a measured value of a detectable label. For example, the detectable label can be a fluorescent moiety and the value can be a luminescence value of the moiety.

In another embodiment, the prognosis of prostate cancer is determined by detecting EpCam, CK (cytokeratin), and/or CD45 expression. In an embodiment, a poor prognosis is provided when EpCam and CK are detected or detected at a high levels, and detection of CD45 is low or absent (i.e., a vesicle that is EpCam+, CK+, CD45−). In another embodiment, the levels of DAB2IP expression, e.g., mRNA or protein, are detected to predict a prognosis of the prostate cancer. DAB2IP expression can be suppressed in prostate cancer and its levels are inversely proportional to stage and prognosis. In yet another embodiment, the levels of EZH2, e.g., mRNA or protein, are assessed to determine whether the prostate cancer is likely to be aggressive or non-aggressive. Low levels of EZH2 can indicate a non-aggressive cancer, e.g., a cancer that does not require aggressive treatment such as surgery or chemotherapy. See Min et al. An oncogene-tumor suppressor cascade drives metastatic prostate cancer by coordinately activating Ras and nuclear factor-KB. Nature Medicine 16:286-294 (2010).

Integrin levels can be assessed to characterize a prostate cancer. In some embodiments, a method of characterizing a prostate cancer, e.g., to determine whether the cancer is indolent or aggressive, comprises assessing the levels of alpha2 beta1 integrin. The integrins can be assessed as vesicle surface markers or as internal vesicle payload, e.g., by detecting integrin mRNA.

One of skill will appreciate that multiple biomarkers disclosed herein can be assessed to determine a biosignature to predict a prognosis or aggressiveness of a prostate cancer. In some embodiments, a method to predict a prognosis or aggressiveness of prostate cancer comprises assessing levels of one or more of EpCam, CK, CD45, DAB2IP, EZH2, alpha2 beta1 integrin, or other markers disclosed herein.

The methods of the invention can be used to distinguish a stage or grade of a prostate cancer. Prostate cancer staging is a process of categorizing the risk of cancer spread beyond the prostate. Such spread is related to the probability of being cured with local therapies such as surgery or radiation. The information considered in such prognostic classification is based on clinical and pathological factors, including physical examination, imaging studies, blood tests and/or biopsy examination.

The most common scheme used to stage prostate cancer is promulgated by the American Joint Committee on Cancer, and is referred to as the TNM system. The TNM system evaluates the size of the tumor, the extent of involved lymph nodes, metastasis and also takes into account cancer grade. As with many other cancers, the cancers are often grouped by stage, e.g., stages I-IV). Generally, Stage I disease is cancer that is found incidentally in a small part of the sample when prostate tissue was removed for other reasons, such as benign prostatic hypertrophy, and the cells closely resemble normal cells and the gland feels normal to the examining finger. In Stage II more of the prostate is involved and a lump can be felt within the gland. In Stage III, the tumor has spread through the prostatic capsule and the lump can be felt on the surface of the gland. In Stage IV disease, the tumor has invaded nearby structures, or has spread to lymph nodes or other organs.

The Whitmore-Jewett stage is another staging scheme that is now used less often. The Gleason Grading System is based on cellular content and tissue architecture from biopsies, which provides an estimate of the destructive potential and ultimate prognosis of the disease.

The TNM tumor classification system can be used to describe the extent of cancer in a subject's body. T describes the size of the tumor and whether it has invaded nearby tissue, N describes regional lymph nodes that are involved, and M describes distant metastasis. TNM is maintained by the International Union Against Cancer (UICC) and is used by the American Joint Committee on Cancer (AJCC) and the International Federation of Gynecology and Obstetrics (FIGO). Those of skill in the art understand that not all tumors have TNM classifications such as, e.g., brain tumors. Generally, T (a,is,(0), 1-4) is measured as the size or direct extent of the primary tumor. N (0-3) refers to the degree of spread to regional lymph nodes: N0 means that tumor cells are absent from regional lymph nodes, N1 means that tumor cells spread to the closest or small numbers of regional lymph nodes, N2 means that tumor cells spread to an extent between N1 and N3; N3 means that tumor cells spread to most distant or numerous regional lymph nodes. M (0/1) refers to the presence of metastasis: MX means that distant metastasis was not assessed; M0 means that no distant metastasis are present; M1 means that metastasis has occurred to distant organs (beyond regional lymph nodes). M1 can be further delineated as follows: M1a indicates that the cancer has spread to lymph nodes beyond the regional ones; M1b indicates that the cancer has spread to bone; and M1c indicates that the cancer has spread to other sites (regardless of bone involvement). Other parameters may also be assessed. G (1-4) refers to the grade of cancer cells (i.e., they are low grade if they appear similar to normal cells, and high grade if they appear poorly differentiated). R (0/1/2) refers to the completeness of an operation (i.e., resection-boundaries free of cancer cells or not). L (0/1) refers to invasion into lymphatic vessels. V (0/1) refers to invasion into vein. C (1-4) refers to a modifier of the certainty (quality) of V.

Prostate tumors are often assessed using the Gleason scoring system. The Gleason scoring system is based on microscopic tumor patterns assessed by a pathologist while interpreting a biopsy specimen. When prostate cancer is present in the biopsy, the Gleason score is based upon the degree of loss of the normal glandular tissue architecture (i.e. shape, size and differentiation of the glands). The classic Gleason scoring system has five basic tissue patterns that are technically referred to as tumor “grades.” The microscopic determination of this loss of normal glandular structure caused by the cancer is represented by a grade, a number ranging from 1 to 5, with 5 being the worst grade. Grade 1 is typically where the cancerous prostate closely resembles normal prostate tissue. The glands are small, well-formed, and closely packed. At Grade 2 the tissue still has well-formed glands, but they are larger and have more tissue between them, whereas at Grade 3 the tissue still has recognizable glands, but the cells are darker. At high magnification, some of these cells in a Grade 3 sample have left the glands and are beginning to invade the surrounding tissue. Grade 4 samples have tissue with few recognizable glands and many cells are invading the surrounding tissue. For Grade 5 samples, the tissue does not have recognizable glands, and are often sheets of cells throughout the surrounding tissue.

miRs that distinguish metastatic and non-metastatic prostate cancer can be overexpressed in metastatic samples versus non-metastatic. Alternately, miRs that distinguish metastatic and non-metastatic prostate cancer can be overexpressed in non-metastatic samples versus metastatic. Useful miRs for distinguishing metastatic prostate cancer include one or more of miR-495, miR-10a, miR-30a, miR-570, miR-32, miR-885-3p, miR-564, and miR-134. In another embodiment, miRs for distinguishing metastatic prostate cancer include one or more of hsa-miR-375, hsa-miR-452, hsa-miR-200b, hsa-miR-146b-5p, hsa-miR-1296, hsa-miR-17*, hsa-miR-100, hsa-miR-574-3p, hsa-miR-20a*, hsa-miR-572, hsa-miR-1236, hsa-miR-181a, hsa-miR-937, and hsa-miR-23a*. In still another embodiment, useful miRs for distinguishing metastatic prostate cancer include one or more of hsa-miR-200b, hsa-miR-375, hsa-miR-582-3p, hsa-miR-17*, hsa-miR-1296, hsa-miR-20a*, hsa-miR-100, hsa-miR-452, and hsa-miR-577. The miRs for distinguishing metastatic prostate cancer can be one or more of miR-141, miR-375, miR-200b and miR-574-3p.

In another aspect, microRNAs (miRs) are used to differentiate between cancer and non-cancer samples. Useful miRs for distinguishing cancer from non-cancer include one or more of hsa-miR-574-3p, hsa-miR-331-3p, hsa-miR-326, hsa-miR-181a-2*, hsa-miR-130b, hsa-miR-301a, hsa-miR-141, hsa-miR-432, hsa-miR-107, hsa-miR-628-5p, hsa-miR-625*, hsa-miR-497, and hsa-miR-484. In another embodiment, useful miRs for distinguishing cancer from non-cancer include one or more of hsa-miR-574-3p, hsa-miR-141, hsa-miR-331-3p, hsa-miR-432, hsa-miR-326, hsa-miR-2110, hsa-miR-107, hsa-miR-130b, hsa-miR-301a, and hsa-miR-625*. In still another embodiment, the useful miRs for distinguishing cancer from non-cancer include one or more of hsa-miR-107, hsa-miR-326, hsa-miR-432, hsa-miR-574-3p, hsa-miR-625*, hsa-miR-2110, hsa-miR-301a, hsa-miR-141 or hsa-miR-373*. The biosignature for distinguishing cancer from non-cancer can comprise one or more of miR-148a, miR-122, miR-146a, miR-22, and miR-24.

The biosignatures of the invention can comprise multiple markers. For example, multiple protein markers and miRs can be used to distinguish prostate cancer from normal, BPH and PCa, or metastatic versus non-metastatic disease. In this manner, improved sensitivity, specificity, and/or accuracy can be obtained. In some embodiments, the levels of one or more of hsa-miR-432, hsa-miR-143, hsa-miR-424, hsa-miR-204, hsa-miR-581f and hsa-miR-451 are detected in a patient sample to assess the presence of prostate cancer. Any of these miRs can be elevated in patients with PCa but having serum PSA <4.0 ng/ml. In an embodiment, the invention provides a method of assessing a prostate cancer, comprising determining a level of one or more of hsa-miR-432, hsa-miR-143, hsa-miR-424, hsa-miR-204, hsa-miR-581f and hsa-miR-451 in a sample from a subject. The sample can be a bodily fluid, e.g., blood, plasma or serum. The miRs can be isolated in vesicles isolated from the sample. The subject can have a PSA level less than some threshold, such as 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5.0, 5.2, 5.4, 5.6, 5.8, or 6.0 ng/ml in a blood sample. Higher levels of the miRs than in a reference sample can indicate the presence of PCa in the sample. In some embodiments, the reference comprises a level of the one or more miRs in control samples from subjects without PCa. In some embodiments, the reference comprises a level of the one or more miRs in control samples from subject with PCa and PSA levels ≧some threshold, such as 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5.0, 5.2, 5.4, 5.6, 5.8, or 6.0 ng/ml. The threshold can be 4.0 ng/ml.

The invention provides for assessing a prostate disorder comprising detecting a presence or level of one or more circulating biomarker selected from the biomarkers listed above. The one or more circulating biomarker can also be selected from BCMA, CEACAM-1, HVEM, IL-1 R4, IL-10 Rb, Trappin-2, p53, hsa-miR-103, hsa-miR-106b, hsa-miR-10b, hsa-miR-125b, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-145, hsa-miR-151-5p, hsa-miR-152, hsa-miR-15a, hsa-miR-181a, hsa-miR-1979, hsa-miR-199a-3p, hsa-miR-19a, hsa-miR-200a, hsa-miR-20b, hsa-miR-29a, hsa-miR-29b, hsa-miR-30a, hsa-miR-329, hsa-miR-335, hsa-miR-361-3p, hsa-miR-365, hsa-miR-373, hsa-miR-423-5p, hsa-miR-502-5p, hsa-miR-595, hsa-miR-663, hsa-miR-671-5p, hsa-miR-760, hsa-miR-7a, hsa-miR-7c, hsa-miR-888, hsa-miR-99a, and a combination thereof. The one or more circulating biomarkers can be selected from the following: hsa-miR-100, hsa-miR-1236, hsa-miR-1296, hsa-miR-141, hsa-miR-146b-5p, hsa-miR-17*, hsa-miR-181a, hsa-miR-200b, hsa-miR-20a*, hsa-miR-23a*, hsa-miR-331-3p, hsa-miR-375, hsa-miR-452, hsa-miR-572, hsa-miR-574-3p, hsa-miR-577, hsa-miR-582-3p, hsa-miR-937, miR-10a, miR-134, miR-141, miR-200b, miR-30a, miR-32, miR-375, miR-495, miR-564, miR-570, miR-574-3p, miR-885-3p, and a combination thereof. Further still, the one or more circulating biomarkers can be selected from the following: hsa-let-7b, hsa-miR-107, hsa-miR-1205, hsa-miR-1270, hsa-miR-130b, hsa-miR-141, hsa-miR-143, hsa-miR-148b*, hsa-miR-150, hsa-miR-154*, hsa-miR-181a*, hsa-miR-181a-2*, hsa-miR-18a*, hsa-miR-19b-1*, hsa-miR-204, hsa-miR-2110, hsa-miR-215, hsa-miR-217, hsa-miR-219-2-3p, hsa-miR-23b*, hsa-miR-299-5p, hsa-miR-301a, hsa-miR-301a, hsa-miR-326, hsa-miR-331-3p, hsa-miR-365*, hsa-miR-373*, hsa-miR-424, hsa-miR-424*, hsa-miR-432, hsa-miR-450a, hsa-miR-451, hsa-miR-484, hsa-miR-497, hsa-miR-517*, hsa-miR-517a, hsa-miR-518f, hsa-miR-574-3p, hsa-miR-595, hsa-miR-617, hsa-miR-625*, hsa-miR-628-5p, hsa-miR-629, hsa-miR-634, hsa-miR-769-5p, hsa-miR-93, hsa-miR-96. The circulating biomarkers can be one or more of hsa-miR-1974, hsa-miR-27b, hsa-miR-103, hsa-miR-146a, hsa-miR-22, hsa-miR-382, hsa-miR-23a, hsa-miR-376c, hsa-miR-335, hsa-miR-142-5p, hsa-miR-221, hsa-miR-142-3p, hsa-miR-151-3p, hsa-miR-21 and hsa-miR-16. In an embodiment, the circulating biomarkers comprise one or more of CD9, PSMA, PCSA, CD63, CD81, B7H3, IL 6, OPG-13, IL6R, PA2G4, EZH2, RUNX2, SERPINB3, and EpCam. The biomarkers can comprise one or more of FOX01A, SOX9, CLNS1A, PTGDS, XPO1, LETMD1, RAD23B, ABCC3, APC, CHES1, EDNRA, FRZB, HSPG2, and TMPRSS2-ETV1 fusion. See WO2010056993, which application is incorporated by reference herein in its entirety. In another embodiment, the circulating biomarkers comprise one or more of A33, a33 n15, AFP, ALA, ALIX, ALP, AnnexinV, APC, ASCA, ASPH (246-260), ASPH (666-680), ASPH (A-10), ASPH (D01P), ASPH (D03), ASPH (G-20), ASPH(H-300), AURKA, AURKB, B7H3, B7H4, BCA-225, BCNP1, BDNF, BRCA, CA125 (MUC16), CA-19-9, C-Bir, CD1.1, CD10, CD174 (Lewis y), CD24, CD44, CD46, CD59 (MEM-43), CD63, CD66e CEA, CD73, CD81, CD9, CDA, CDAC1 1a2, CEA, C-Erb2, C-erbB2, CRMP-2, CRP, CXCL12, CYFRA21-1, DLL4, DR3, EGFR, Epcam, EphA2, EphA2 (H-77), ER, ErbB4, EZH2, FASL, FRT, FRT c.f23, GDF15, GPCR, GPR30, Gro-alpha, HAP, HBD 1, HBD2, HER 3 (ErbB3), HSP, HSP70, hVEGFR2, iC3b, IL 6 Unc, IL-1B, 1L6 Unc, IL6R, IL8, IL-8, INSIG-2, KLK2, L1CAM, LAMN, LDH, MACC-1, MAPK4, MART-1, MCP-1, M-CSF, MFG-E8, MIC1, MIF, MIS RII, MMG, MMP26, MMP7, MMP9, MS4A1, MUC1, MUC1 seq1, MUC1 seq11A, MUC17, MUC2, Ncam, NGAL, NPGP/NPFF2, OPG, OPN, p53, p53, PA2G4, PBP, PCSA, PDGFRB, PGP9.5, PIM1, PR (B), PRL, PSA, PSMA, PSME3, PTEN, R5-CD9 Tube 1, Reg IV, RUNX2, SCRN1, seprase, SERPINB3, SPARC, SPB, SPDEF, SRVN, STAT 3, STEAP1, TF (FL-295), TFF3, TGM2, TIMP-1, TIMP1, TIMP2, TMEM211, TMPRSS2, TNF-alpha, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK, UNC93A, VEGF A, and YPSMA-1. Any combination of these markers can be used in a biosignature to assess a prostate cancer. The circulating biomarkers can be associated with vesicles, e.g., vesicle surface markers or vesicle payload. The prostate disorders include without limitation a benign disorder such as BPH, or prostate cancer, including cancers of various stages and grades. See, e.g., Table 6.

TABLE 6 Biomarkers for Prostate Disorders Illustrative Disorder Biomarkers Benign Prostate BCMA, CEACAM-1, HVEM, IL-1 R4, IL-10 Rb, Trappin-2, p53, hsa-miR-329, Hyperplasia (BPH) hsa-miR-30a, hsa-miR-335, hsa-miR-152, hsa-miR-151-5p, hsa-miR-200a, hsa- miR-145, hsa-miR-29a, hsa-miR-106b, hsa-miR-595, hsa-miR-142-5p, hsa-miR- 99a, hsa-miR-20b, hsa-miR-373, hsa-miR-502-5p, hsa-miR-29b, hsa-miR-142-3p, hsa-miR-663, hsa-miR-423-5p, hsa-miR-15a, hsa-miR-888, hsa-miR-361-3p, hsa- miR-365, hsa-miR-10b, hsa-miR-199a-3p, hsa-miR-181a, hsa-miR-19a, hsa-miR- 125b, hsa-miR-760, hsa-miR-7a, hsa-miR-671-5p, hsa-miR-7c, hsa-miR-1979, hsa-miR-103 Metastatic Prostate hsa-miR-100, hsa-miR-1236, hsa-miR-1296, hsa-miR-141, hsa-miR-146b-5p, hsa- Cancer miR-17*, hsa-miR-181a, hsa-miR-200b, hsa-miR-20a*, hsa-miR-23a*, hsa-miR- 331-3p, hsa-miR-375, hsa-miR-452, hsa-miR-572, hsa-miR-574-3p, hsa-miR-577, hsa-miR-582-3p, hsa-miR-937, miR-10a, miR-134, miR-141, miR-200b, miR-30a, miR-32, miR-375, miR-495, miR-564, miR-570, miR-574-3p, miR-885-3p Metastatic Prostate hsa-miR-200b, hsa-miR-375, hsa-miR-141, hsa-miR-331-3p, hsa-miR-181a, hsa- Cancer miR-574-3p Metastatic Prostate FOX01A, SOX9, CLNS1A, PTGDS, XPO1, LETMD1, RAD23B, ABCC3, APC, Cancer CHES1, EDNRA, FRZB, HSPG2, TMPRSS2_ETV1 fusion Prostate Cancer hsa-let-7b, hsa-miR-107, hsa-miR-1205, hsa-miR-1270, hsa-miR-130b, hsa-miR- 141, hsa-miR-143, hsa-miR-148b*, hsa-miR-150, hsa-miR-154*, hsa-miR-181a*, hsa-miR-181a-2*, hsa-miR-18a*, hsa-miR-19b-1*, hsa-miR-204, hsa-miR-2110, hsa-miR-215, hsa-miR-217, hsa-miR-219-2-3p, hsa-miR-23b*, hsa-miR-299-5p, hsa-miR-301a, hsa-miR-301a, hsa-miR-326, hsa-miR-331-3p, hsa-miR-365*, hsa- miR-373*, hsa-miR-424, hsa-miR-424*, hsa-miR-432, hsa-miR-450a, hsa-miR- 451, hsa-miR-484, hsa-miR-497, hsa-miR-517*, hsa-miR-517a, hsa-miR-518f, hsa-miR-574-3p, hsa-miR-595, hsa-miR-617, hsa-miR-625*, hsa-miR-628-5p, hsa-miR-629, hsa-miR-634, hsa-miR-769-5p, hsa-miR-93, hsa-miR-96 Prostate Cancer CD9, PSMA, PCSA, CD63, CD81, B7H3, IL 6, OPG-13, IL6R, PA2G4, EZH2, RUNX2, SERPINB3, EpCam Prostate Cancer A33, a33 n15, AFP, ALA, ALIX, ALP, AnnexinV, APC, ASCA, ASPH (246- 260), ASPH (666-680), ASPH (A-10), ASPH (D01P), ASPH (D03), ASPH (G- 20), ASPH (H-300), AURKA, AURKB, B7H3, B7H4, BCA-225, BCNP1, BDNF, BRCA, CA125 (MUC16), CA-19-9, C-Bir, CD1.1, CD10, CD174 (Lewis y), CD24, CD44, CD46, CD59 (MEM-43), CD63, CD66e CEA, CD73, CD81, CD9, CDA, CDAC1 1a2, CEA, C-Erb2, C-erbB2, CRMP-2, CRP, CXCL12, CYFRA21- 1, DLL4, DR3, EGFR, Epcam, EphA2, EphA2 (H-77), ER, ErbB4, EZH2, FASL, FRT, FRT c.f23, GDF15, GPCR, GPR30, Gro-alpha, HAP, HBD1, HBD2, HER3 (ErbB3), HSP, HSP70, hVEGFR2, iC3b, IL6 Unc, IL-1B, IL6 Unc, IL6R, IL8, IL-8, INSIG-2, KLK2, L1CAM, LAMN, LDH, MACC-1, MAPK4, MART-1, MCP-1, M-CSF, MFG-E8, MIC1, MIF, MIS RII, MMG, MMP26, MMP7, MMP9, MS4A1, MUC1, MUC1 seq1, MUC1 seq11A, MUC17, MUC2, Ncam, NGAL, NPGP/NPFF2, OPG, OPN, p53, p53, PA2G4, PBP, PCSA, PDGFRB, PGP9.5, PIM1, PR (B), PRL, PSA, PSMA, PSME3, PTEN, R5-CD9 Tube 1, Reg IV, RUNX2, SCRN1, seprase, SERPINB3, SPARC, SPB, SPDEF, SRVN, STAT3, STEAP1, TF (FL-295), TFF3, TGM2, TIMP-1, TIMP1, TIMP2, TMEM211, TMPRSS2, TNF-alpha, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK, UNC93A, VEGFA, YPSMA-1 Prostate Cancer Vesicle 5T4, ACTG1, ADAM10, ADAM15, ALDOA, ANXA2, ANXA6, APOA1, Markers ATP1A1, BASP1, C1orf58, C20orf114, C8B, CAPZA1, CAV1, CD151, CD2AP, CD59, CD9, CD9, CFL1, CFP, CHMP4B, CLTC, COTL1, CTNND1, CTSB, CTSZ, CYCS, DPP4, EEF1A1, EHD1, ENO1, F11R, F2, F5, FAM125A, FNBP1L, FOLH1, GAPDH, GLB1, GPX3, HIST1H1C, HIST1H2AB, HSP90AB1, HSPA1B, HSPA8, IGSF8, ITGB1, ITIH3, JUP, LDHA, LDHB, LUM, LYZ, MFGE8, MGAM, MMP9, MYH2, MYL6B, NME1, NME2, PABPC1, PABPC4, PACSIN2, PCBP2, PDCD6IP, PRDX2, PSA, PSMA, PSMA1, PSMA2, PSMA4, PSMA6, PSMA7, PSMB1, PSMB2, PSMB3, PSMB4, PSMB5, PSMB6, PSMB8, PTGFRN, RPS27A, SDCBP, SERINC5, SH3GL1, SLC3A2, SMPDL3B, SNX9, TACSTD1, TCN2, THBS1, TPI1, TSG101, TUBB, VDAC2, VPS37B, YWHAG, YWHAQ, YWHAZ Prostate Cancer Treatment hsa-miR-1974, hsa-miR-27b, hsa-miR-103, hsa-miR-146a, hsa-miR-22, hsa-miR- 382, hsa-miR-23a, hsa-miR-376c, hsa-miR-335, hsa-miR-142-5p, hsa-miR-221, hsa-miR-142-3p, hsa-miR-151-3p, hsa-miR-21, hsa-miR-16 Prostate Cancer E2F3, c-met, pRB, EZH2, e-cad, CAXII, CAIX, HIF-1α, Jagged, PIM-1, hepsin, RECK, Clusterin, MMP9, MTSP-1, MMP24, MMP15, IGFBP-2, IGFBP-3, E2F4, caveolin, EF-1A, Kallikrein 2, Kallikrein 3, PSGR

Any combination of these markers can be used in a biosignature to assess a prostate disorder, such as BPH and prostate cancer. The biosignature can also be used to assess the stage or grade of the prostate cancer.

The prostate cancer can be characterizing using one or more processes disclosed herein with at least 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, or 70% sensitivity. The prostate cancer can be characterized with at least 80, 81, 82, 83, 84, 85, 86, or 87% sensitivity. For example, the prostate cancer can be characterized with at least 87.1, 87.2, 87.3, 87.4, 87.5, 87.6, 87.7, 87.8, 87.9, 88.0, or 89% sensitivity, such as with at least 90% sensitivity, such as at least 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% sensitivity.

The prostate cancer of a subject can also be characterized with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97% specificity, such as with at least 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100% specificity.

The prostate cancer can also be characterized with at least 70% sensitivity and at least 80, 90, 95, 99, or 100% specificity; at least 80% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 85% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 86% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 87% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 88% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 89% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 90% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 95% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 99% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; or at least 100% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity.

In some embodiments, the biosignature characterizes a phenotype of a subject with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97% accuracy, such as with at least 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100% accuracy.

In some embodiments, the biosignature characterizes a phenotype of a subject with an AUC of at least 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, or 0.97, such as with at least 0.971, 0.972, 0.973, 0.974, 0.975, 0.976, 0.977, 0.978, 0.978, 0.979, 0.980, 0.981, 0.982, 0.983, 0.984, 0.985, 0.986, 0.987, 0.988, 0.989, 0.99, 0.991, 0.992, 0.993, 0.994, 0.995, 0.996, 0.997, 0.998, 0.999 or 1.00.

Furthermore, the confidence level for determining the specificity, sensitivity, accuracy and/or AUC can be determined with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% confidence.

Gastrointestinal Cancer

The gastrointestinal (GI) tract includes without limitation the oral cavity, gums, pharynx, tongue, salivary glands, esophagus, pancreas, liver, gallbladder, small intestine (duodenum, jejunum, ileum), bile duct, stomach, large intestine (cecum, colon, rectum), appendix and anus. The biosignature can be used to detect or characterize cancers of such components, e.g., colorectal cancer (CRC), stomach cancer, intestinal cancer, liver cancer or esophageal cancer. A gastrointestinal (GI) tract biosignature can comprise any one or more antigens for as listed in FIG. 1, any one or more binding agents associated with isolating a vesicle for characterizing colon cancer (for example, as shown in FIG. 2), any one or more additional biomarkers, such as shown in FIG. 6.

Colorectal Cancer

Although colonoscopy is the gold standard to screen and identify colorectal cancer (CRC), it is estimated half of patients who are recommended for colonoscopy are not compliant. Often the lack of compliance is because many perceive a colonoscopy as an uncomfortable and invasive procedure. A less invasive diagnostic test that could identify those patients that have a blood-based biosignature indicative of the need for detection and biopsy by colonoscopy could improve compliance. This strategy would result in cancers being identified earlier and prevent disease-free individuals from undergoing an unnecessary invasive procedure. Current blood-based tests rely on increased levels of either carcinoembryonic antigen (CEA) or carbohydrate antigenic determinant (CA 19-9). Unfortunately, CEA and CA 19-9 are neither organ-specific nor tumor-specific. The present invention improves upon these markers using vesicle-based detection assays.

The present invention provides methods to identify subjects likely to have or having CRC using a biosignature derived from a sample from the subject. The sample can be bodily fluids such as blood, plasma or serum, or stool. The biosignature can contain circulating biomarkers, including biomarkers associated with vesicles. The biosignature may contain mutations detected by sequencing nucleic acids, e.g., RNAs contained within vesicles.

In one aspect of the invention, biosignatures are derived from vesicles isolated from plasma of patients with and without CRC. Vesicle surface proteins are used in a multiplex assay to capture and detect vesicles. The quantity of vesicles with significant concentrations of these surface proteins leads to the development of a vesicle-specific biosignature that can differentiate CRC samples from normal. Such vesicles present in blood plasma of CRC patients provide a signature by which CRC can be diagnosed as early as histological grade 1. In some embodiments, vesicles are captured with antibodies to various surface proteins. The capture vesicles can be detected with vesicle specific markers, e.g., one or more of CD9, CD81, and CD63. In some embodiments, the vesicle-based biosignature comprises measuring the level of one or more of CD9, CD81, CD63, EpCam, EGFR, and STEAP. In some embodiments, one or more of the following markers are used to capture and/or detect vesicles: CD9, NGAL, CD81, STEAP, CD24, A33, CD66E, EPHA2, TMEM211, TROP2, TROP2, EGFR, DR3, UNC93A, MUC17, EpCAM, MUC17, CD63, B7H3. In some embodiments, one or more of the following markers are used to capture and/or detect vesicles: TMEM211, MUC1, GPR110 (GPCR 110), CD24, CD9, CD81, and CD63. In some embodiments, one or more of the following markers are used to capture and/or detect vesicles: DR3, STEAP, epha2, TMEM211, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, and TETS. In some embodiments, TMEM211 is used to capture and/or detect vesicles. In some embodiments, MUC1 is used to capture and/or detect vesicles. In some embodiments, GPR110 is used to capture and/or detect vesicles. In some embodiments, CD24 is used to capture and/or detect vesicles. In some embodiments, one or more of TMEM211, MUC1, GPR110 (GPCR 110), and CD24 are used to capture vesicles and one or more general vesicle marker is used to detection the captured vesicles.

In another aspect of the invention, microRNAs (miRs) associated with vesicles are used to determine a biosignature. The miRs can be derived from vesicles, e.g., exosomes, isolated from a patient sample, e.g., blood. In some embodiments, one or more of the following miRs are used to derive a CRC biosignature: miR 92, miR 21, miR 9 and miR 491.

In still another aspect of the invention, the payload within a vesicle is assessed. KRAS and BRAF mutation screening can be used for colon cancer monitoring from tumor samples. As shown in Example 4, KRAS mutations are found in RNA derived from colon cell line vesicles. Example 5 shows that KRAS can be sequenced in RNA vesicles derived from plasma samples. In some embodiments, sequencing of KRAS and/or BRAF nucleic acid within vesicles can be used to detect CRC. The nucleic acid can be RNA, e.g., mRNA. A CRC biosignature can comprise sequencing of KRAS and BRAF RNA isolated from vesicles.

A colon cancer biosignature can comprise any one or more antigens for colon cancer as listed in FIG. 1, any one or more binding agents associated with isolating or detecting a vesicle for characterizing colon cancer (for example, as shown in FIG. 2), any one or more additional biomarkers, such as shown in FIG. 6.

The biosignature can comprise one or more miRNA selected from the group consisting of miR-24-1, miR-29b-2, miR-20a, miR-10a, miR-32, miR-203, miR-106a, miR-17-5p, miR-30c, miR-223, miR-126, miR-128b, miR-21, miR-24-2, miR-99b, miR-155, miR-213, miR-150, miR-107, miR-191, miR-221, miR-20a, miR-510, miR-92, miR-513, miR-19a, miR-21, miR-20, miR-183, miR-96, miR-135b, miR-31, miR-21, miR-92, miR-222, miR-181b, miR-210, miR-20a, miR-106a, miR-93, miR-335, miR-338, miR-133b, miR-346, miR-106b, miR-153a, miR-219, miR-34a, miR-99b, miR-185, miR-223, miR-211, miR-135a, miR-127, miR-203, miR-212, miR-95, or miR-17-5p, or any combination thereof. The biosignature can also comprise one or more underexpressed miRs such as miR-143, miR-145, miR-143, miR-126, miR-34b, miR-34c, let-7, miR-9-3, miR-34a, miR-145, miR-455, miR-484, miR-101, miR-145, miR-133b, miR-129, miR-124a, miR-30-3p, miR-328, miR-106a, miR-17-5p, miR-342, miR-192, miR-1, miR-34b, miR-215, miR-192, miR-301, miR-324-5p, miR-30a-3p, miR-34c, miR-331, miR-148b, miR-548c-5p, miR-362-3p and miR422a.

The biosignature can comprise assessing one or more genes, such as EFNB1, ERCC1, HER2, VEGF, and EGFR. A biomarker mutation for colon cancer that can be assessed in a vesicle can also include one or more mutations of EGFR, KRAS, VEGFA, B-Raf, APC, or p53. The biosignature can also comprise one or more proteins, ligands, or peptides that can be assessed of a vesicle, such as AFRs, Rabs, ADAM10, CD44, NG2, ephrin-B1, MIF, b-catenin, Junction, plakoglobin, glalectin-4, RACK1, tetrspanin-8, FasL, TRAIL, A33, CEA, EGFR, dipeptidase 1, hsc-70, tetraspanins, ESCRT, TS, PTEN, or TOPO1.

A vesicle can be isolated and assayed for to provide a diagnostic, prognostic or theranostic profile, such as the stage of the cancer, the efficacy of the cancer, or other characteristics of the cancer. Alternatively, the esicle can be directly assayed from a sample, such that the vesicles are not purified or concentrated prior to assaying for a biosignature associated with colon cancer.

As depicted in FIG. 69, a G1 cancer, such as colon cancer, a biosignature can comprise detection of EpCam, CD63, CD81, CD9, CD66, or any combination thereof, of a vesicle. Furthermore, a colon cancer-biosignature for various stages of cancer can comprise CD63, CD9, EpCam, or any combination thereof (see for example, FIGS. 71 and 72). For example, the biosignature can comprise CD9 and EpCam. In some embodiments, the G1 cancer biosignature comprises one or more miRNA selected from the group consisting of miR-548c-5p, miR-362-3p, miR-422a, miR-597, miR-429, miR-200a, and miR-200b. These miRNAs can be overexpressed in G1 cancers, as shown in FIG. 110. The miRNA signature can be combined with the biomarkers listed above. The biosignatures can provide a diagnostic, prognostic or theranostic profile, such as the stage of the cancer, the efficacy of the cancer, or other characteristics of the cancer.

The invention provides for assessing a gastrointestinal disorder comprising detecting a presence or level of one or more circulating biomarker selected from the biomarkers listed above. The one or more circulating biomarker can also be selected from CD9, EGFR, NGAL, CD81, STEAP, CD24, A33, CD66E, EPHA2, Ferritin, GPR30, GPR110, MMP9, OPN, p53, TMEM211, TROP2, TGM2, TIMP, EGFR, DR3, UNC93A, MUC17, EpCAM, MUC1, MUC2, TSG101, CD63, B7H3, and a combination thereof. The one or more circulating biomarkers can be selected from the following: DR3, STEAP, epha2, TMEM211, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, TETS, and a combination thereof. Further still, the one or more circulating biomarkers can be selected from the following: A33, AFP, ALIX, ALX4, ANCA, APC, ASCA, AURKA, AURKB, B7H3, BANK1, BCNP1, BDNF, CA-19-9, CCSA-2, CCSA-3&4, CD10, CD24, CD44, CD63, CD66 CEA, CD66e CEA, CD81, CD9, CDA, C-Erb2, CRMP-2, CRP, CRTN, CXCL12, CYFRA21-1, DcR3, DLL4, DR3, EGFR, Epcam, EphA2, FASL, FRT, GAL3, GDF15, GPCR (GPR110), GPR30, GRO-1, HBD 1, HBD2, HNP1-3, IL-1B, 1L8, IMP3, L1CAM, LAMN, MACC-1, MGC20553, MCP-1, M-CSF, MIC1, MIF, MMP7, MMP9, MS4A1, MUC1, MUC17, MUC2, Ncam, NGAL, NNMT, OPN, p53, PCSA, PDGFRB, PRL, PSMA, PSME3, Reg IV, SCRN1, Sept-9, SPARC, SPON2, SPR, SRVN, TFF3, TGM2, TIMP-1, TMEM211, TNF-alpha, TPA, TPS, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK, UNC93A, and VEGFA, and a combination thereof. In an embodiment, the circulating biomarkers comprise one or more of miR 92, miR 21, miR 9 and miR 491, and/or one or more of hsa-miR-376c, hsa-miR-215, hsa-miR-652, hsa-miR-582-5p, hsa-miR-324-5p, hsa-miR-1296, hsa-miR-28-5p, hsa-miR-190, hsa-miR-590-5p, hsa-miR-202, and hsa-miR-195. In another embodiment, the circulating biomarkers comprise one or more of TMEM211, MUC1, CD24 and/or GPR110 (GPCR 110). The circulating biomarkers can be associated with vesicles, e.g., vesicle surface markers or vesicle payload. The gastrointestinal disorders include without limitation a benign disorder such as benign polyps, or a cancer such as colorectal cancer, including cancers of various stages and grades. See, e.g., Table 7.

TABLE 7 Biomarkers for Gastrointestinal Disorders Illustrative Disorder Biomarkers Colorectal cancer CD9, EGFR, NGAL, CD81, STEAP, CD24, A33, CD66E, EPHA2, Ferritin, GPR30, GPR110, MMP9, OPN, p53, TMEM211, TROP2, TGM2, TIMP, EGFR, DR3, UNC93A, MUC17, EpCAM, MUC1, MUC2, TSG101, CD63, B7H3 Colorectal cancer DR3, STEAP, epha2, TMEM211, unc93A, A33, CD24, NGAL, EpCam, MUC17, TROP2, TETS Colorectal cancer A33, AFP, ALIX, ALX4, ANCA, APC, ASCA, AURKA, AURKB, B7H3, BANK1, BCNP1, BDNF, CA-19-9, CCSA-2, CCSA-3&4, CD10, CD24, CD44, CD63, CD66 CEA, CD66e CEA, CD81, CD9, CDA, C-Erb2, CRMP-2, CRP, CRTN, CXCL12, CYFRA21-1, DcR3, DLL4, DR3, EGFR, Epcam, EphA2, FASL, FRT, GAL3, GDF15, GPCR (GPR110), GPR30, GRO-1, HBD1, HBD2, HNP1-3, IL-1B, IL8, IMP3, L1CAM, LAMN, MACC-1, MGC20553, MCP-1, M-CSF, MIC1, MIF, MMP7, MMP9, MS4A1, MUC1, MUC17, MUC2, Ncam, NGAL, NNMT, OPN, p53, PCSA, PDGFRB, PRL, PSMA, PSME3, Reg IV, SCRN1, Sept-9, SPARC, SPON2, SPR, SRVN, TFF3, TGM2, TIMP-1, TMEM211, TNF-alpha, TPA, TPS, Trail-R2, Trail-R4, TrKB, TROP2, Tsg101, TWEAK, UNC93A, VEGFA Colorectal cancer miR 92, miR 21, miR 9 and miR 491 Colorectal cancer TMEM211, MUC1, CD24 and/or GPR110 (GPCR 110) Colorectal cancer hsa-miR-376c, hsa-miR-215, hsa-miR-652, hsa-miR-582-5p, hsa-miR-324-5p, hsa- miR-1296, hsa-miR-28-5p, hsa-miR-190, hsa-miR-590-5p, hsa-miR-202, hsa-miR- 195 Colorectal cancer A26C1A, A26C1B, A2M, ACAA2, ACE, ACOT7, ACP1, ACTA1, ACTA2, ACTB, vesicle markers ACTBL2, ACTBL3, ACTC1, ACTG1, ACTG2, ACTN1, ACTN2, ACTN4, ACTR3, ADAM10, ADSL, AGR2, AGR3, AGRN, AHCY, AHNAK, AKR1B10, ALB, ALDH16A1, ALDH1A1, ALDOA, ANXA1, ANXA11, ANXA2, ANXA2P2, ANXA4, ANXA5, ANXA6, AP2A1, AP2A2, APOA1, ARF1, ARF3, ARF4, ARF5, ARF6, ARHGDIA, ARPC3, ARPC5L, ARRDC1, ARVCF, ASCC3L1, ASNS, ATP1A1, ATP1A2, ATP1A3, ATP1B1, ATP4A, ATP5A1, ATP5B, ATP5I, ATP5L, ATP5O, ATP6AP2, B2M, BAIAP2, BAIAP2L1, BRI3BP, BSG, BUB3, C1orf58, C5orf32, CAD, CALM1, CALM2, CALM3, CAND1, CANX, CAPZA1, CBR1, CBR3, CCT2, CCT3, CCT4, CCT5, CCT6A, CCT7, CCT8, CD44, CD46, CD55, CD59, CD63, CD81, CD82, CD9, CDC42, CDH1, CDH17, CEACAM5, CFL1, CFL2, CHMP1A, CHMP2A, CHMP4B, CKB, CLDN3, CLDN4, CLDN7, CLIC1, CLIC4, CLSTN1, CLTC, CLTCL1, CLU, COL12A1, COPB1, COPB2, CORO1C, COX4I1, COX5B, CRYZ, CSPG4, CSRP1, CST3, CTNNA1, CTNNB1, CTNND1, CTTN, CYFIP1, DCD, DERA, DIP2A, DIP2B, DIP2C, DMBT1, DPEP1, DPP4, DYNC1H1, EDIL3, EEF1A1, EEF1A2, EEF1AL3, EEF1G, EEF2, EFNB1, EGFR, EHD1, EHD4, EIF3EIP, EIF3I, EIF4A1, EIF4A2, ENO1, ENO2, ENO3, EPHA2, EPHA5, EPHB1, EPHB2, EPHB3, EPHB4, EPPK1, ESD, EZR, F11R, F5, F7, FAM125A, FAM125B, FAM129B, FASLG, FASN, FAT, FCGBP, FER1L3, FKBP1A, FLNA, FLNB, FLOT1, FLOT2, G6PD, GAPDH, GARS, GCN1L1, GDI2, GK, GMDS, GNA13, GNAI2, GNAI3, GNAS, GNB1, GNB2, GNB2L1, GNB3, GNB4, GNG12, GOLGA7, GPA33, GPI, GPRC5A, GSN, GSTP1, H2AFJ, HADHA, hCG_1757335, HEPH, HIST1H2AB, HIST1H2AE, HIST1H2AJ, HIST1H2AK, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E, HIST1H4F, HIST1H4H, HIST1H4I, HIST1H4J, HIST1H4K, HIST1H4L, HIST2H2AC, HIST2H4A, HIST2H4B, HIST3H2A, HIST4H4, HLA-A, HLA- A29.1, HLA-B, HLA-C, HLA-E, HLA-H, HNRNPA2B1, HNRNPH2, HPCAL1, HRAS, HSD17B4, HSP90AA1, HSP90AA2, HSP90AA4P, HSP90AB1, HSP90AB2P, HSP90AB3P, HSP90B1, HSPA1A, HSPA1B, HSPA1L, HSPA2, HSPA4, HSPA5, HSPA6, HSPA7, HSPA8, HSPA9, HSPD1, HSPE1, HSPG2, HYOU1, IDH1, IFITM1, IFITM2, IFITM3, IGH@, IGHG1, IGHG2, IGHG3, IGHG4, IGHM, IGHV4-31, IGK@, IGKC, IGKV1-5, IGKV2-24, IGKV3-20, IGSF3, IGSF8, IQGAP1, IQGAP2, ITGA2, ITGA3, ITGA6, ITGAV, ITGB1, ITGB4, JUP, KIAA0174, KIAA1199, KPNB1, KRAS, KRT1, KRT10, KRT13, KRT14, KRT15, KRT16, KRT17, KRT18, KRT19, KRT2, KRT20, KRT24, KRT25, KRT27, KRT28, KRT3, KRT4, KRT5, KRT6A, KRT6B, KRT6C, KRT7, KRT75, KRT76, KRT77, KRT79, KRT8, KRT9, LAMA5, LAMP1, LDHA, LDHB, LFNG, LGALS3, LGALS3BP, LGALS4, LIMA1, LIN7A, LIN7C, LOC100128936, LOC100130553, LOC100133382, LOC100133739, LOC284889, LOC388524, LOC388720, LOC442497, LOC653269, LRP4, LRPPRC, LRSAM1, LSR, LYZ, MAN1A1, MAP4K4, MARCKS, MARCKSL1, METRNL, MFGE8, MICA, MIF, MINK1, MITD1, MMPI, MOBKL1A, MSN, MTCH2, MUC13, MYADM, MYH10, MYH11, MYH14, MYH9, MYL6, MYL6B, MYO1C, MYO1D, NARS, NCALD, NCSTN, NEDD4, NEDD4L, NME1, NME2, NOTCH1, NQO1, NRAS, P4HB, PCBP1, PCNA, PCSK9, PDCD6, PDCD6IP, PDIA3, PDXK, PEBP1, PFN1, PGK1, PHB, PHB2, PKM2, PLEC1, PLEKHB2, PLSCR3, PLXNA1, PLXNB2, PPIA, PPIB, PPP2R1A, PRDX1, PRDX2, PRDX3, PRDX5, PRDX6, PRKAR2A, PRKDC, PRSS23, PSMA2, PSMC6, PSMD11, PSMD3, PSME3, PTGFRN, PTPRF, PYGB, QPCT, QSOX1, RAB10, RAB11A, RAB11B, RAB13, RAB14, RAB15, RAB1A, RAB1B, RAB2A, RAB33B, RAB35, RAB43, RAB4B, RAB5A, RAB5B, RAB5C, RAB6A, RAB6B, RAB7A, RAB8A, RAB8B, RAC1, RAC3, RALA, RALB, RAN, RANP1, RAP1A, RAP1B, RAP2A, RAP2B, RAP2C, RDX, REG4, RHOA, RHOC, RHOG, ROCK2, RP11-631M21.2, RPL10A, RPL12, RPL6, RPL8, RPLP0, RPLP0-like, RPLP1, RPLP2, RPN1, RPS13, RPS14, RPS15A, RPS16, RPS18, RPS20, RPS21, RPS27A, RPS3, RPS4X, RPS4Y1, RPS4Y2, RPS7, RPS8, RPSA, RPSAP15, RRAS, RRAS2, RUVBL1, RUVBL2, S100A10, S100A11, S100A14, S100A16, S100A6, S100P, SDC1, SDC4, SDCBP, SDCBP2, SERINC1, SERINC5, SERPINA1, SERPINF1, SETD4, SFN, SLC12A2, SLC12A7, SLC16A1, SLC1A5, SLC25A4, SLC25A5, SLC25A6, SLC29A1, SLC2A1, SLC3A2, SLC44A1, SLC7A5, SLC9A3R1, SMPDL3B, SNAP23, SND1, SOD1, SORT1, SPTAN1, SPTBN1, SSBP1, SSR4, TACSTD1, TAGLN2, TBCA, TCEB1, TCP1, TF, TFRC, THBS1, TJP2, TKT, TMED2, TNFSF10, TNIK, TNKS1BP1, TNPO3, TOLLIP, TOMM22, TPI1, TPM1, TRAP1, TSG101, TSPAN1, TSPAN14, TSPAN15, TSPAN6, TSPAN8, TSTA3, TTYH3, TUBA1A, TUBA1B, TUBA1C, TUBA3C, TUBA3D, TUBA3E, TUBA4A, TUBA4B, TUBAE, TUBB, TUBB2A, TUBB2B, TUBB2C, TUBB3, TUBB4, TUBB4Q, TUBB6, TUFM, TXN, UBA1, UBA52, UBB, UBC, UBE2N, UBE2V2, UGDH, UQCRC2, VAMP1, VAMP3, VAMP8, VCP, VIL1, VPS25, VPS28, VPS35, VPS36, VPS37B, VPS37C, WDR1, YWHAB, YWHAE, YWHAG, YWHAH, YWHAQ, YWHAZ MicroRNAs miR-24-1, miR-29b-2, miR-20a, miR-10a, miR-32, miR-203, miR-106a, miR-17-5p, miR-30c, miR-223, miR-126, miR-128b, miR-21, miR-24-2, miR-99b, miR-155, miR-213, miR-150, miR-107, miR-191, miR-221, miR-20a, miR-510, miR-92, miR- 513, miR-19a, miR-21, miR-20, miR-183, miR-96, miR-135b, miR-31, miR-21, miR-92, miR-222, miR-181b, miR-210, miR-20a, miR-106a, miR-93, miR-335, miR-338, miR-133b, miR-346, miR-106b, miR-153a, miR-219, miR-34a, miR-99b, miR-185, miR-223, miR-211, miR-135a, miR-127, miR-203, miR-212, miR-95, or miR-17-5p, miR-143, miR-145, miR-143, miR-126, miR-34b, miR-34c, let-7, miR- 9-3, miR-34a, miR-145, miR-455, miR-484, miR-101, miR-145, miR-133b, miR- 129, miR-124a, miR-30-3p, miR-328, miR-106a, miR-17-5p, miR-342, miR-192, miR-1, miR-34b, miR-215, miR-192, miR-301, miR-324-5p, miR-30a-3p, miR-34c, miR-331, miR-148b, miR-548c-5p, miR-362-3p, miR422a MicroRNAs hsa-miR-548c-5p, hsa-miR-362-3p, hsa-miR-422a, hsa-miR-597, hsa-miR-429, hsa- miR-200a, hsa-miR-200b, hsa-miR-376c, hsa-miR-652, hsa-miR-221*, hsa-miR- 215, hsa-miR-324-5p, hsa-miR-376c, hsa-miR-652, hsa-miR-324-5p, hsa-miR-28- 5p, hsa-miR-190, hsa-miR-590-5p, hsa-miR-202, hsa-miR-195, hsa-miR-215, hsa- miR-582-5p, hsa-miR-1296

The colorectal cancer can be characterized using one or more processes disclosed herein with at least 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, or 70% sensitivity. The colorectal cancer can be characterized with at least 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, or 87% sensitivity. For example, the colorectal cancer can be characterized with at least 87.1, 87.2, 87.3, 87.4, 87.5, 87.6, 87.7, 87.8, 87.9, 88.0, or 89% sensitivity, such as with at least 90% sensitivity, such as at least 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% sensitivity.

The colorectal cancer of a subject can also be characterized with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97% specificity, such as with at least 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100% specificity.

The colorectal cancer can also be characterized with at least 70% sensitivity and at least 80, 90, 95, 99, or 100% specificity; at least 80% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 85% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 86% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 87% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 88% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 89% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 90% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 95% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 99% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; or at least 100% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity.

Furthermore, the confidence level for determining the specificity, sensitivity, and/or other statistical performance measures may be with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% confidence.

Breast Cancer

The present invention provides methods to identify subjects likely to have or having breast cancer using a biosignature derived from a sample from the subject. The sample can be bodily fluids such as blood, plasma or serum, or breast milk. The biosignature can contain circulating biomarkers, including biomarkers associated with vesicles. The biosignature may contain mutations detected by sequencing nucleic acids, e.g., RNAs contained within vesicles.

The invention provides a method for characterizing a breast cancer comprising detecting a presence or level of one or more circulating biomarker selected from the biomarkers listed herein. The method can be used to provide characterizations such as diagnosis, prognosis or theranosis. The circulating biomarkers can be associated with vesicles, e.g., vesicle surface markers or vesicle payload. Illustrative circulating biomarkers for use in characterizing breast cancer are shown in Table 8.

TABLE 8 Biomarkers for Breast Cancer Disorder Biomarkers Breast cancer miR-21, miR-155, miR-206, miR-122a, miR-210, miR-21, miR-155, miR-206, miR- 122a, miR-210, let-7, miR-10b, miR-125a, miR-125b, miR-145, miR-143, miR-145, miR-1b Breast cancer GAS5 Breast cancer ER, PR, HER2, MUC1, EGFR, KRAS, B-Raf, CYP2D6, hsp70, MART-1, TRP, HER2, hsp70, MART-1, TRP, HER2, ER, PR, Class III b-tubulin, VEGFA, ETV6- NTRK3, BCA-225, hsp70, MARTI, ER, VEGFA, Class III b-tubulin, HER2/neu (e.g., for Her2+ breast cancer), GPR30, ErbB4 (JM) isoform, MPR8, MISIIR, CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, progesterone receptor (PR) or its isoform (PR(A) or PR(B)), P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA- 2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2, Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR Breast cancer CD9, MIS Rii, ER, CD63, MUC1, HER3, STAT3, VEGFA, BCA, CA125, CD24, EPCAM, ERB B4 Breast cancer CD10, NPGP/NPFF2, HER2/ERBB2, AGTR1, NPY1R, neurokinin receptor-1 (NK- 1 or NK-1R), NK-2, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1 Breast cancer SPB, SPC, NSE, PGP9.5, CD9, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, EGFR, B7H3, IC3b, MUC1, mesothelin, SPA, PCSA, CD63, STEAP, AQP5, CD81, DR3, PSM, GPCR, EphA2, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, A33, CD24, CD10, NGAL, EpCam, MUC17, TROP-2, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR Breast cancer BRCA, MUC-1, MUC16, CD24, ErbB4, ErbB2 (HER2), ErbB3, HSP70, Mammaglobin, PR, PR(B), VEGFA Breast cancer CD9, HSP70, Gal3, MIS, EGFR, ER, ICB3, CD63, B7H4, MUC1, DLL4, CD81, ERB3, VEGF, BCA225, BRCA, CA125, CD174, CD24, ERB2, NGAL, GPR30, CYFRA21, CD31, cMET, MUC2, ERBB4 Breast cancer CD9, EphA2, EGFR, B7H3, PSMA, PCSA, CD63, STEAP, CD81, STEAP1, ICAM1 (CD54), PSMA, A33, DR3, CD66e, MFG-8e, TMEM211, TROP-2, EGFR, Mammoglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, NK-2, EpCam, NK-1R, PSMA, 5T4, PAI-1, CD45 Breast cancer PGP9.5, CD9, HSP70, gal3-b2c10, EGFR, iC3b, PSMA, PCSA, CD63, MUC1, DLL4, CD81, B7-H3, HER3 (ErbB3), MART-1, PSA, VEGF A, TIMP-1, GPCR GPR110, EphA2, MMP9, mmp7, TMEM211, UNC93a, BRCA, CA125 (MUC16), Mammaglobin, CD174 (Lewis y), CD66e CEA, CD24 c.sn3, C-erbB2, CD10, NGAL, epcam, CEA (carcinoembryonic Antigen), GPR30, CYFRA21-1, OPN, MUC17, hVEGFR2, MUC2, NCAM, ASPH, ErbB4, SPB, SPC, CD9, MS4A1, EphA2, MIS RII, HER2 (ErbB2), ER, PR (B), MRP8, CD63, B7H4, TGM2, CD81, DR3, STAT3, MACC-1, TrKB, IL6 Unc, OPG-13, IL6R, EZH2, SCRN1, TWEAK, SERPINB3, CDAC1, BCA-225, DR3, A33, NPGP/NPFF2, TIMP1, BDNF, FRT, Ferritin heavy chain, seprase, p53, LDH, HSP, ost, p53, CXCL12, HAP, CRP, Gro-alpha, Tsg101, GDF15 Breast cancer CD9, HSP70, Gal3, MIS (RII), EGFR, ER, ICB3, CD63, B7H4, MUC1, CD81, ERB3, MART1, STAT3, VEGF, BCA225, BRCA, CA125, CD174, CD24, ERB2, NGAL, GPR30, CYFRA21, CD31, cMET, MUC2, ERB4, TMEM211 Breast cancer 5T4 (trophoblast), ADAM10, AGER/RAGE, APC, APP (β-amyloid), ASPH (A-10), B7H3 (CD276), BACE1, BAI3, BRCA1, BDNF, BIRC2, C1GALT1, CA125 (MUC16), Calmodulin 1, CCL2 (MCP-1), CD9, CD10(BD), CD127 (IL7R), CD174, CD24, CD44, CD63, CD81, CEA, CRMP-2, CXCR3, CXCR4, CXCR6, CYFRA21, derlin 1, DLL4, DPP6, E-CAD, EpCAM, EphA2 (H-77), ER(1) ESR1α, ER(2) ESR2β, Erb B4, Erbb2, erb3 (Erb-B3) PA2G4, Gal3, GPR30 (G-coupled ER1), HAP1, HER3, HSP-27, HSP70, IC3b, IL8, junction plakoglobin, Keratin 15, Mammaglobin, MART1, MCT2, MFGE8 as detector Ab-PE, MMP9, MRP8, Muc1, MUC17, MUC2, NCAM, NG2 (CSPG4), Ngal, NHE-3, NTSE (CD73), ODC1, OPG, OPN (SC), p53, PARK7, PCSA, PGP9.5 (PARKS), PR(B), PSA, PSMA, RAGE, STXBP4, Survivin, TFF3 (secreted), TIMP1, TIMP2, TMEM211, TRAF4 (scaffolding), TRAIL-R2 (death Receptor 5), TrkB, Tsg101, UNC93a, VEGF A, VEGFR2, YB-1, VEGFR1, 5HT2B (serotonin receptor 2B), BRCA1, BACE1, CDH1-cadherin Breast Cancer Gal3 and/or BCA200; NCAM and/or OPN Breast Cancer CD44, CD9, EpCam, CD24, Muc1, BCA200, Gal3 Breast Cancer cMV markers (tetraspanin⁺, CD45⁺, FasL⁺, CTLA4⁺); angiogenic cMV markers (tetraspanin⁺, CD31⁺, DLL4⁺, VEGFR2⁺, HIF2a⁺, Tie2⁺, Ang1⁺); metastatic cMV markers (tetraspanin⁺, Muc1⁺, CD147⁺, TIMP1⁺, TIMP2⁺, MMP7⁺, MMP9⁺) Breast Cancer AK5.2, ATP6V1B1, CRABP1 Breast Cancer DST.3, GATA3, KRT81 Breast Cancer AK5.2, ATP6V1B1, CRABP1, DST.3, ELF5, GATA3, KRT81, LALBA, OXTR, RASL10A, SERHL, TFAP2A.1, TFAP2A.3, TFAP2C, VTCN1 Proteins associated with HSPA8, GAPDH, ANXA2, HSP90AA1, GLC1F, ALB, Lamp1, PRDX1, TUBA1A, breast cancer cMVs PRDX2, CLIC1, GNB2, HSPA5, CCT2, HSPB1, MUC1, P4HB, TUBB, FGG, PDIA3, Flot2, HSPD1, NRAS, PFN2, SERPINF1, C4A, CFH, COMT, G6PD, PHB, RPLP0, ACSL3, ACTB, ACTB, ACTG1, ACTG1, BTG1, C1QC, CRABP2, ERBB2, ETFA, HNRNPA2B1, HSPA9, IGHG1, NEB, SMC3, TUFM, AKAP9, CLASP1, DLD, GLUD1, GNB5, H2-M3, HSD17B10, HTATIP2, IMPDH2, MARVELD2, MYH8, PTX3, SNX25, ZNF571 Breast Cancer TRAP; Renal Cell Carcinoma; Filamin; 14.3.3, Pan; Prohibitin; c-fos; Ang-2; GSTmu; Ang-1; FHIT; Rad51; Inhibin alpha; Cadherin-P; 14.3.3 gamma; p18INK4c; P504S; XRCC2; Caspase 5; CREB-Binding Protein; Estrogen Receptor; IL17; Claudin 2; Keratin 8; GAPDH; CD1; Keratin, LMW; Gamma Glutamylcysteine Synthetase(GCS)/Glutamate-cysteine Ligase; a-B-Crystallin; Pax- 5; MMP-19; APC; IL-3; Keratin 8 (phospho-specific Ser73); TGF-beta 2; ITK; Oct- 2/; DJ-1; B7-H2; Plasma Cell Marker; Rad18; Estriol; Chk1; Prolactin Receptor; Laminin Receptor; Histone H1; CD45RO; GnRH Receptor; IP10/CRG2; Actin, Muscle Specific; S100; Dystrophin; Tubulin-a; CD3zeta; CDC37; GABA a Receptor 1; MMP-7 (Matrilysin); Heregulin; Caspase 3; CD56/NCAM-1; Gastrin 1; SREBP-1 (Sterol Regulatory Element Binding Protein-1); MLH1; PGP9.5; Factor VIII Related Antigen; ADP-ribosylation Factor (ARF-6); MHC II (HLA-DR) Ia; Survivin; CD23; G-CSF; CD2; Calretinin; Neuron Specific Enolase; CD165; Calponin; CD95/Fas; Urocortin; Heat Shock Protein 27/hsp27; Topo II beta; Insulin Receptor; Keratin 5/8; sm; Actin, skeletal muscle; CA19-9; GluR1; GRIP1; CD79a mb-1; TdT; HRP; CD94; CCK-8; Thymidine Phosphorylase; CD57; Alkaline Phosphatase (AP); CD59/ MACIF/MIRL/Protectin; GLUT-1; alpha-1-antitrypsin; Presenillin; Mucin 3 (MUC3); pS2; 14-3-3 beta; MMP-13 (Collagenase-3); Fli-1; mGluR5; Mast Cell Chymase; Laminin B1/b1; Neurofilament (160 kDa); CNPase; Amylin Peptide; Gai1; CD6; alpha-1-antichymotrypsin; E2F-2; MyoD1 Ductal carcinoma in situ Laminin B1/b1; E2F-2; TdT; Apolipoprotein D; Granulocyte; Alkaline Phosphatase (DCIS) (AP); Heat Shock Protein 27/hsp27; CD95/Fas; pS2; Estriol; GLUT-1; Fibronectin; CD6; CCK-8; sm; Factor VIII Related Antigen; CD57; Plasminogen; CD71/ Transferrin Receptor; Keratin 5/8; Thymidine Phosphorylase; CD45/T200/LCA; Epithelial Specific Antigen; Macrophage; CD10; MyoD1; Gai1; bc1-XL; hPL; Caspase 3; Actin, skeletal muscle; IP10/CRG2; GnRH Receptor; p35nck5a; ADP- ribosylation Factor (ARF-6); Cdk4; alpha-1-antitrypsin; IL17; Neuron Specific Enolase; CD56/NCAM-1; Prolactin Receptor; Cdk7; CD79a mb-1; Collagen IV; CD94; Myeloid Specific Marker; Keratin 10; Pax-5; IgM (m-Heavy Chain); CD45RO; CA19-9; Mucin 2; Glucagon; Mast Cell Chymase; MLH1; CD1; CNPase; Parkin; MHC II (HLA-DR) Ia; B7-H2; Chk1; Lambda Light Chain; MHC II (HLA- DP and DR); Myogenin; MMP-7 (Matrilysin); Topo II beta; CD53; Keratin 19; Rad18; Ret Oncoprotein; MHC II (HLA-DP); E3-binding protein (ARM1); Progesterone Receptor; Keratin 8; IgG; IgA; Tubulin; Insulin Receptor Substrate-1; Keratin 15; DR3; IL-3; Keratin 10/13; Cyclin D3; MHC I (HLA25 and HLA-Aw32); Calmodulin; Neurofilament (160 kDa) Ductal carcinoma in situ Macrophage; Fibronectin; Granulocyte; Keratin 19; Cyclin D3; CD45/T200/LCA; (DCIS) v. other Breast EGFR; Thrombospondin; CD81/TAPA-1; Ruv C; Plasminogen; Collagen IV; cancer Laminin B1/b1; CD10; TdT; Filamin; bcl-XL; 14.3.3 gamma; 14.3.3, Pan; p170; Apolipoprotein D; CD71/Transferrin Receptor; FHIT

The breast cancer can be characterized using one or more processes disclosed herein with at least 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, or 70% sensitivity. The breast cancer can be characterized with at least 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, or 87% sensitivity. For example, the breast cancer can be characterized with at least 87.1, 87.2, 87.3, 87.4, 87.5, 87.6, 87.7, 87.8, 87.9, 88.0, or 89% sensitivity, such as with at least 90% sensitivity, such as at least 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% sensitivity.

The breast cancer of a subject can also be characterized with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97% specificity, such as with at least 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100% specificity.

The breast cancer can also be characterized with at least 70% sensitivity and at least 80, 90, 95, 99, or 100% specificity; at least 80% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 85% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 86% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 87% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 88% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 89% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 90% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 95% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 99% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; or at least 100% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity.

Furthermore, the confidence level for determining the specificity, sensitivity, and/or other statistical performance measures may be with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% confidence.

Ovarian Cancer

A biosignature for characterizing ovarian cancer can comprise an antigen associated with ovarian cancer (for example, as shown in FIG. 1), and one or more additional biomarkers, such as shown in FIG. 4. In one embodiment, a biosignature for ovarian cancer can comprise one or more antigens associated with ovarian cancer, such as, but not limited to, CD24, CA125, VEGF1, VEGFR2, HER2, MISIIR, or any combination thereof. The biosignature for ovarian cancer can comprise one or more of the aforementioned antigens and one or more additional biomarker, such as, but not limited to miRNA, mRNA, DNA, or any combination thereof. The biosignature for ovarian cancer can comprise one or more antigens associated with ovarian cancer, such as, but not limited to, CD24, CA125, VEGF1, VEGFR2, HER2, MISIIR, or any combination thereof, with one or more miRNA biomarkers, such as, but not limited to, miR-200a, miR-141, miR-200c, miR-200b, miR-21, miR-141, miR-200a, miR-200b, miR-200c, miR-203, miR-205, miR-214, miR-215, miR-199a, miR-140, miR-145, miR-125b-1, or any combination thereof.

A biosignature for ovarian cancer can comprise one or more antigens associated with ovarian cancer, such as, but not limited to, CD24, CA125, VEGF1, VEGFR2, HER2, MISIIR, or any combination thereof, with one or more miRNA biomarkers (such as the aforementioned miRNA), mRNAs (such as, but not limited to, ERCC1, ER, TOPO1, TOP2A, AR, PTEN, HER2/neu, EGFR), mutations (including, but not limited to, those relating to KRAS and/or B-Raf) or any combination thereof.

Proteins associated with ovarian cancer vesicles include HSP90AA1, GLC1F, CLDN3, CLDN4, CLDN4, and CLDN5. One or more of these proteins can be assessed to characterize an ovarian cancer.

A vesicle can be isolated, assayed or both, for one or more miRNA and one or more antigens associated with ovarian cancer to provide a diagnostic, prognostic or theranostic profile. Alternatively, the vesicle can be directly assayed from a sample, such that the vesicle is not purified or concentrated prior to assaying for one or more miRNA or antigens associated with ovarian cancer.

Lung Cancer

The present invention provides methods to identify subjects likely to have or having lung cancer using a biosignature derived from a sample from the subject. The sample can be bodily fluids such as blood, plasma or serum, sputum, mucous or lavage. The biosignature can contain circulating biomarkers, including biomarkers associated with vesicles. The biosignature may contain mutations detected by sequencing nucleic acids, e.g., RNAs contained within vesicles.

The invention provides a method for characterizing a lung cancer comprising detecting a presence or level of one or more circulating biomarker selected from the biomarkers listed herein. The method can be used to provide characterizations such as diagnosis, prognosis or theranosis. The circulating biomarkers can be associated with vesicles, e.g., vesicle surface markers or vesicle payload. Illustrative circulating biomarkers for use in characterizing lung cancer are shown in Table 9.

TABLE 9 Biomarkers for Lung Cancer Disorder Biomarkers Lung cancer A33, ADAM28, APC, APC, AQP5, B7H3, BDNF, CABYR, CD10, CD24, CD3, CD63, CD66 CEA, CD66e, CD81, CD9, CDADC1, CEA, CEACAM, C-Erb, C- ERBB2, CHI3L1, CRP, CXCL12, DLL4, DR3, EGFR, EpCam, EphA2, Ferritin, FRT, Gal3, GDF15, GPCR GPR110, GPR30, GRO-1, Gro-alpha, Haptoglobin (HAP), HSP70, iC3b, LDH, MACC1, Mesothelin, MMP7, MMP9, MS4A1, MUC1, MUC17, MUC2, NCAM, NDUFB7, N-gal, NSE, Osteopontin (OPN), P2RX7, P53, PCSA, PGP9.5, PRL, PSMA, PTP, SCRN1, Seprase, SPA, SPB, SPC, SPR, TFF3, TGM2, TIMP-1, TMEM211, TPA, TrKB, TROP2, tsg 101, TWEAK, UNC93, UNC93a Lung cancer hsa-miR-125a-5p, hsa-miR-650, hsa-miR-194, hsa-miR-1200, hsa-miR-326, hsa- miR-30b*, hsa-miR-19a, hsa-miR-7a*, hsa-miR-708*, hsa-miR-99a, hsa-miR-199b- 5p, hsa-miR-543, hsa-miR-7i*, hsa-miR-518c*, hsa-miR-642, hsa-miR-654-3p, hsa- miR-518d-5p, hsa-miR-1266, hsa-miR-154, hsa-miR-662, hsa-miR-523, hsa-miR- 198, hsa-miR-920, hsa-miR-885-3p, hsa-miR-99a*, hsa-miR-337-3p, hsa-miR-363 Lung cancer miR-497 Lung cancer DR3, PRB and MS4A1 Lung cancer PRB, MACC1 Lung cancer SPB, SPC, TFF3, PGP9.5, CD9, MS4A1, NDUFB7, Cal3, iC3b, CD63, MUC1, TGM2, CD81, B7H3, DR3, MACC1, TrkB, TIMP1, GPCR (GPR110), MMP9, MMP7, TMEM211, TWEAK, CDADC1, UNC93, APC, A33, CD66e, TIMP1, CD24, ErbB2, CD10, BDNF, Ferritin, Ferritin, Seprase, NGAL, EpCam, ErbB2, Osteopontin (OPN), LDH, OPN, HSP70, OPN, OPN, OPN, OPN, MUC2, NCAM, CXCL12, Haptoglobin (HAP), CRP, Gro-alpha Lung cancer SPB, SPC, NSE, PGP9.5, CD9, P2RX7, NDUFB7, NSE, Gal3, Osteopontin, CHI3L1, EGFR, B7H3, iC3b, MUC1, Mesothelin, SPA, TPA, PCSA, CD63, AQP5, DLL4, CD81, DR3, PSMA, GPCR 110 (GPR110), EPHA2, CEACAM, PTP, CABYR, TMEM211, ADAM28, UNC93a, A33, CD24, CD10, NGAL, EpCam, MUC17, TROP2, MUC2 Lung cancer SPB, SPC, PSP9.5, NDUFB7, Gal3, iC3b, MUC1, GPCR 110, CABYR, MUC17 Lung cancer CD9, CD63, CD81, B7H3, PRO GRP, CYTO 18, FTH1, TGM2, CENPH, ANNEXIN I, ANNEXIN V, ERB2, EGFR, CRP, VEGF, CYTO 19, CCL2, Osteopontin (OST19), Osteopontin (OST22), BTUB, CD45, TIMP, NACC1, MMP9, BRCA1, P27, NSE, M2PK, HCG, MUC1, CEA, CEACAM, CYTO 7, EPCAM, MS4A1, MUC1, MUC2, PGP9, SPA, SPA, SPD, P53, GPCR (GPR110), SFTPC, UNCR2, NSE, INGA3, INTG b4, MMP1, PNT, RACK1, NAP2, HLA, BMP2, PTH1R, PAN ADH, NCAM, CD151, CKS1, FSHR, HIF, KRAS, LAMP2, SNAIL, TRIM29, TSPAN1, TWIST1, ASPH, AURKB Lung cancer PRO GRP, MMP9, CENPH Lung cancer miR-21, miR-205, miR-221 (protective), let-7a (protective), miR-137 (risky), miR- 372 (risky), or miR-122a (risky), or any combination thereof. The biosignature can comprise one or more upregulated or overexpressed miRNAs, such as miR-17-92, miR-19a, miR-21, miR-92, miR-155, miR-191, miR-205 or miR-210; one or more downregulated or underexpressed miRNAs, such as miR-let-7, or any combination thereof. The one or more biomarker may be miR-92a-2*, miR-147, miR-574-5p Lung cancer EGFR, PTEN, RRM1, RRM2, ABCB1, ABCG2, LRP, VEGFR2, VEGFR3, class III b-tubulin Lung cancer EGFR, KRAS, B-Raf, UGT1A1 Lung cancer KRAS, hENT1 Lung cancer SPB, SPC, PSP9.5, NDUFB7, ga13-b2c10, iC3b, MUC1, GPCR, CABYR, muc17 Lung cancer midkine (MK or MDK) Lung cancer RLF-MYCL1, TGF-ALK, CD74-ROS1 NSCLC cancer BRAF, BRCA1, cMET, EGFR, EGFR w/T790M, EML4-ALK, ERCC1, Her2 Exon treatment associated 20 insert, KRAS, MSH2, PIK3CA, PTEN, ROS1 (trans), RRM1, TLE3, TS, markers VEGFR2 Lung cancer Pgrmc1 (progesterone receptor membrane component 1)/sigma-2 receptor, STEAP, EZH2 Lung cancer Prohibitin, CD23, Amylin Peptide, HRP, Rad51, Pax-5, Oct-3/, GLUT-1, PSCA, Thrombospondin, FHIT, a-B-Crystallin, LewisA, Vacular Endothelial Growth Factor(VEGF), Hepatocyte Factor Homologue-4, Flt-4, GluR6/7, Prostate Apoptosis Response Protein-4, GluR1, Fli-1, Urocortin, S100A4, 14-3-3 beta, P504S, HDAC1, PGP9.5, DJ-1, COX2, MMP-19, Actin, skeletal muscle, Claudin 3, Cadherin-P, Collagen IX, p27Kip1, Cathepsin D, CD30 (Reed-Sternberg Cell Marker), Ubiquitin, FSH-b, TrxR2, CCK-8, Cyclin C, CD138, TGF-beta 2, Adrenocorticotrophic Hormone, PPAR-gamma, Bcl-6, GLUT-3, IGF-I, mRANKL, Fas-ligand, Filamin, Calretinin, Oct-1, Parathyroid Hormone, Claudin 5, Claudin 4, Raf-1 (Phospho-specific), CDC14A Phosphatase, Mitochondria, APC, Gastrin 1, Ku (p80), Gail, XPA, Maltose Binding Protein, Melanoma (gp100), Phosphotyrosine, Amyloid A, CXCR4/Fusin, Hepatic Nuclear Factor-3B, Caspase 1, HPV 16-E7, Axonal Growth Cones, Lck, Ornithine Decarboxylase, Gamma Glutamylcysteine Synthetase(GCS)/Glutamate-cysteine Ligase, ERCC1, Calmodulin, Caspase 7 (Mch 3), CD137 (4-1BB), Nitric Oxide Synthase, brain (bNOS), E2F-2, IL-10R, L-Plastin, CD18, Vimentin, CD50/ICAM-3, Superoxide Dismutase, Adenovirus Type 5 E1A, PHAS-I, Progesterone Receptor (phospho-specific) - Serine 294, MHC II (HLA- DQ), XPG, ER Ca+2 ATPase2, Laminin-s, E3-binding protein (ARM1), CD45RO, CD1, Cdk2, MMP-10 (Stromilysin-2), sm, Surfactant Protein B (Pro), Apolipoprotein D, CD46, Keratin 8 (phospho-specific Ser73), PCNA, PLAP, CD20, Syk, LH, Keratin 19, ADP-ribosylation Factor (ARF-6), Int-2 Oncoprotein, Luciferase, AIF (Apoptosis Inducing Factor), Grb2, bcl-X, CD16, Paxillin, MHC II (HLA-DP and DR), B-Cell, p21WAF1, MHC II (HLA-DR), Tyrosinase, E2F-1, Pds1, Calponin, Notch, CD26/DPP IV, SV40 Large T Antigen, Ku (p70/p80), Perforin, XPF, SIM Ag (SIMA-4D3), Cdk1/p34cdc2, Neuron Specific Enolase, b-2- Microglobulin, DNA Polymerase Beta, Thyroid Hormone Receptor, Human, Alkaline Phosphatase (AP), Plasma Cell Marker, Heat Shock Protein 70/hsp70, TRP75/gp75, SRF (Serum Response Factor), Laminin B1/b1, Mast Cell Chymase, Caldesmon, CEA/CD66e, CD24, Retinoid X Receptor (hRXR), CD45/T200/LCA, Rabies Virus, Cytochrome c, DR3, bcl-XL, Fascin, CD71/Transferrin Receptor

In an aspect, the invention provides a method of characterizing a lung cancer comprising detecting a level of one or more circulating biomarkers in a sample from a subject selected from the group consisting of A33, ADAM28, APC, APC, AQP5, B7H3, BDNF, CABYR, CD10, CD24, CD3, CD63, CD66 CEA, CD66e, CD81, CD9, CDADC1, CEA, CEACAM, C-Erb, C-ERBB2, CHI3L1, CRP, CXCL12, DLL4, DR3, EGFR, EpCam, EphA2, Ferritin, FRT, Gal3, GDF15, GPCR GPR110, GPR30, GRO-1, Gro-alpha, Haptoglobin (HAP), HSP70, iC3b, LDH, MACC1, Mesothelin, MMP7, MMP9, MS4A1, MUC1, MUC17, MUC2, NCAM, NDUFB7, N-gal, NSE, Osteopontin (OPN), P2RX7, P53, PCSA, PGP9.5, PRL, PSMA, PTP, SCRN1, Seprase, SPA, SPB, SPC, SPR, TFF3, TGM2, TIMP-1, TMEM211, TPA, TrKB, TROP2, tsg 101, TWEAK, UNC93, and UNC93a. Characterizing the cancer may comprise diagnosing, prognosing or theranosing the cancer. The invention further provides a method of predicting response to a therapeutic agent comprising detecting a level of one or more circulating biomarkers in a sample from a subject selected from the group consisting of A33, ADAM28, APC, APC, AQP5, B7H3, BDNF, CABYR, CD10, CD24, CD3, CD63, CD66 CEA, CD66e, CD81, CD9, CDADC1, CEA, CEACAM, C-Erb, C-ERBB2, CHI3L1, CRP, CXCL12, DLL4, DR3, EGFR, EpCam, EphA2, Ferritin, FRT, Gal3, GDF15, GPCR GPR110, GPR30, GRO-1, Gro-alpha, Haptoglobin (HAP), HSP70, iC3b, LDH, MACC1, Mesothelin, MMP7, MMP9, MS4A1, MUC1, MUC17, MUC2, NCAM, NDUFB7, N-gal, NSE, Osteopontin (OPN), P2RX7, P53, PCSA, PGP9.5, PRL, PSMA, PTP, SCRN1, Seprase, SPA, SPB, SPC, SPR, TFF3, TGM2, TIMP-1, TMEM211, TPA, TrKB, TROP2, tsg 101, TWEAK, UNC93, and UNC93a. The therapeutic agent can be a therapeutic agent for treating cancer such as those disclosed herein. The cancer can be a lung cancer.

The lung cancer can be characterized using one or more processes disclosed herein with at least 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, or 70% sensitivity. The lung cancer can be characterized with at least 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, or 87% sensitivity. For example, the lung cancer can be characterized with at least 87.1, 87.2, 87.3, 87.4, 87.5, 87.6, 87.7, 87.8, 87.9, 88.0, or 89% sensitivity, such as with at least 90% sensitivity, such as at least 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% sensitivity.

The lung cancer of a subject can also be characterized with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, or 97% specificity, such as with at least 97.1, 97.2, 97.3, 97.4, 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 99.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9 or 100% specificity.

The lung cancer can also be characterized with at least 70% sensitivity and at least 80, 90, 95, 99, or 100% specificity; at least 80% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 85% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 86% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 87% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 88% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 89% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 90% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 95% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; at least 99% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity; or at least 100% sensitivity and at least 80, 85, 90, 95, 99, or 100% specificity.

Furthermore, the confidence level for determining the specificity, sensitivity, and/or other statistical performance measures may be with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% confidence.

Organ Transplant Rejection and Autoimmune Conditions

A vesicle can also be used for determining phenotypes such as organ distress and/or organ transplant rejection. As used herein organ transplant includes partial organ or tissue transplant. The presence, absence or levels of one or more biomarkers present in a vesicle can be assessed to monitor organ rejection or success. The level or amount of vesicles in the sample can also be used to assess organ rejection or success. The assessment can be determined with at least 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% specificity, sensitivity, or both. For example, the assessment can be determined with at least 97.5, 97.6, 97.7, 97.8, 97.8, 97.9, 98.0, 98.1, 98.2, 98.3, 98.4, 98.5, 98.6, 98.7, 98.8, 98.9, 99.0, 99.1, 998.2, 99.3, 99.4, 99.5, 99.6, 99.7, 99.8, 99.9% sensitivity, specificity, or both

The vesicle can be purified or concentrated prior to analysis. Alternatively, the level, or amount, of vesicles can be directly assayed from a sample, without prior purification or concentration. The vesicle can be quantitated, su. For example, a cell or tissue-specific vesicle can be isolated using one or more binding agents specific for a particular organ. The cell-of-origin specific vesicle can be assessed for one or more molecular features, such as one or more biomarkers associated with organ distress or organ transplant rejection. The presence, absence or levels of one or more biomarkers present, can be assessed to monitor organ rejection or success.

One or more vesicles can be analyzed for the assessment, detection or diagnosis of the rejection of a tissue or organ transplant by a subject. The tissue or organ transplant rejection can be hyperacute, acute, or chronic rejection. The vesicle can also be analyzed for the assessment, detection or diagnosis of graft versus host disease in a subject. The subject can be the recipient of an autogenic, allogenic or xenogenic tissue or organ transplant.

The vesicle can also be analyzed to detect the rejection of a tissue or organ transplant. The vesicle may be produced by the tissue or organ transplant. Such tissues or organs include, but are not limited to, a heart, lung, pancreas, kidney, eye, cornea, muscle, bone marrow, skin, cartilage, bone, appendages, hair, face, tendon, stomach, intestine, vein, artery, differentiated cells, partially differentiated cells or stem cells.

The vesicle can comprise at least one biomarker which is used to assess, diagnose or determine the probability or occurrence of rejection of a tissue or organ transplant by a subject. A biomarker can also be used to assess, diagnose or detect graft versus host disease in a subject. The biomarker can be a protein, a polysaccharide, a fatty acid or a nucleic acid (such as DNA or RNA). The biomarker can be associated with the rejection of a specific tissue or organ or systemic organ failure. More than one biomarker can be analyzed, for example, one or more proteins marker can be analyzed in combination with one or more nucleic acid markers. The biomarker may be an intracellular or extracellular marker.

The vesicle can also be analyzed for at least one marker for the assessment, detection or diagnosis of cell apoptosis or necrosis associated with, or the causation of, rejection of a tissue or organ transplant by a subject.

The presence of a biomarker can be indicative of the rejection of a tissue or an organ by a subject, wherein the biomarker includes, but is not limited to, CD40, CD40 ligand, N-acetylmuramoyl-L-alanine amidase precursor, adiponectin, AMBP protein precursor, C4b-binding protein a-chain precursor, ceruloplasmin precursor, complement C3 precursor, complement component C9 precursor, complement factor D precursor, alpha1-B-glycoprotein, beta2-glycoprotein I precursor, heparin cofactor II precursor, Immunoglobulin mu chain C region protein, Leucine-rich alpha2-glycoprotein precursor, pigment epithelium-derived factor precursor, plasma retinol-binding protein precursor, translation initiation factor 3 subunit 10, ribosomal protein L7, beta-transducin, 1-TRAF, or lysyl-tRNA synthetase.

Rejection of a kidney by a subject can also be detected by analyzing vesicles for the presence of beta-transducin. Rejection of transplanted tissue can also be detected by isolating a cell-of-origin specific vesicles from CD40-expressing cells and detecting for the increase of Bcl-2 or TNFalpha.

Rejection of a liver transplant by a subject can be detected by analyzing the vesiclesfor the presence of an F1 antigen marker. The F1 antigen is, without being bound to theory, specific to liver to and can be used to detect an increase in liver cell-of-origin specific vesicles. This increase can be used as an early indication of organ distress/rejection.

Bronchiolitis obliterans due to bone marrow and/or lung transplantation or other causes, or graft atherosclerosis/graft phlebosclerosis can also be diagnosed by the analysis of a vesicle.

A vesicle can also be analyzed for the detection, diagnosis or assessment of an autoimmune or other immunological reaction-related phenotype in a subject. Examples of such a disorder include, but are not limited to, systemic lupus erythematosus (SLE), discoid lupus, lupus nephritis, sarcoidosis, inflammatory arthritis, including juvenile arthritis, rheumatoid arthritis, psoriatic arthritis, Reiter's syndrome, ankylosing spondylitis, and gouty arthritis, multiple sclerosis, hyper IgE syndrome, polyarteritis nodosa, primary biliary cirrhosis, inflammatory bowel disease, Crohn's disease, celiac's disease (gluten-sensitive enteropathy), autoimmune hepatitis, pernicious anemia, autoimmune hemolytic anemia, psoriasis, scleroderma, myasthenia gravis, autoimmune thrombocytopenic purpura, autoimmune thyroiditis, Grave's disease, Hasimoto's thyroiditis, immune complex disease, chronic fatigue immune dysfunction syndrome (CFIDS), polymyositis and dermatomyositis, cryoglobulinemia, thrombolysis, cardiomyopathy, pemphigus vulgaris, pulmonary interstitial fibrosis, asthma, Churg-Strauss syndrome (allergic granulomatosis), atopic dermatitis, allergic and irritant contact dermatitis, urtecaria, IgE-mediated allergy, atherosclerosis, vasculitis, idiopathic inflammatory myopathies, hemolytic disease, Alzheimer's disease, chronic inflammatory demyelinating polyneuropathy and AIDs.

One or more biomarkers from the vesicles can be used to assess, diagnose or determine the probability of the occurrence of an autoimmune or other immunological reaction-related disorder in a subject. The biomarker can be a protein, a polysaccharide, a fatty acid or a nucleic acid (such as DNA or RNA). The biomarker can be associated with a specific autoimmune disorder, a systemic autoimmune disorder, or other immunological reaction-related disorder. More than one biomarker can be analyzed. For example one or more protein markers can be analyzed in combination with one or more nucleic acid markers. The biomarker can be an intracellular or extracellular marker. The biomarker can also be used to detect, diagnose or assess inflammation.

Analysis of vesicles from subjects can be used identify subjects with inflammation associated with asthma, sarcoidosis, emphysema, cystic fibrosis, idiopathic pulmonary fibrosis, chronic bronchitis, allergic rhinitis and allergic diseases of the lung such as hypersensitivity pneumonitis, eosinophilic pneumonia, as well as pulmonary fibrosis resulting from collagen, vascular, and autoimmune diseases such as rheumatoid arthritis.

Theranosis

As disclosed herein, methods are disclosed for characterizing a phenotype for a subject by assessing one or more biomarkers, including vesicle biomarkers and/or circulating biomarkers. The biomarkers can be assessed using methods for multiplexed analysis of vesicle biomarkers disclosed herein. Characterizing a phenotype can include providing a theranosis for a subject, such as determining if a subject is predicted to respond to a treatment or is predicted to be non-responsive to a treatment. A subject that responds to a treatment can be termed a responder whereas a subject that does not respond can be termed a non-responder. A subject suffering from a condition can be considered to be a responder for a treatment based on, but not limited to, an improvement of one or more symptoms of the condition; a decrease in one or more side effects of an existing treatment; an increased improvement, or rate of improvement, in one or more symptoms as compared to a previous or other treatment; or prolonged survival as compared to without treatment or a previous or other treatment. For example, a subject suffering from a condition can be considered to be a responder to a treatment based on the beneficial or desired clinical results including, but are not limited to, alleviation or amelioration of one or more symptoms, diminishment of extent of disease, stabilized (i.e., not worsening) state of disease, preventing spread of disease, delay or slowing of disease progression, amelioration or palliation of the disease state, and remission (whether partial or total), whether detectable or undetectable. Treatment also includes prolonging survival as compared to expected survival if not receiving treatment or if receiving a different treatment.

The systems and methods disclosed herein can be used to select a candidate treatment for a subject in need thereof. Selection of a therapy can be based on one or more characteristics of a vesicle, such as the biosignature of a vesicle, the amount of vesicles, or both. Vesicle typing or profiling, such as the identification of the biosignature of a vesicle, the amount of vesicles, or both, can be used to identify one or more candidate therapeutic agents for an individual suffering from a condition. For example, vesicle profiling can be used to determine if a subject is a non-responder or responder to a particular therapeutic, such as a cancer therapeutic if the subject is suffering from a cancer.

Vesicle profiling can be used to provide a diagnosis or prognosis for a subject, and a therapy can be selected based on the diagnosis or prognosis. Alternatively, therapy selection can be directly based on a subject's vesicle profile. Furthermore, a subject's vesicle profile can be used to follow the evolution of a disease, to evaluate the efficacy of a medication, adapt an existing treatment for a subject suffering from a disease or condition, or select a new treatment for a subject suffering from a disease or condition.

A subject's response to a treatment can be assessed using biomarkers, including vesicles, microRNA, and other circulating biomarkers. In one embodiment, a subject is determined, classified, or identified as a non-responder or responder based on the subject's vesicle profile assessed prior to any treatment. During pretreatment, a subject can be classifed as a non-responder or responder, thereby reducing unnecessary treatment options, and avoidance of possible side effects from ineffective therapeutics. Furthermore, the subject can be identified as a responder to a particular treatment, and thus vesicle profiling can be used to prolong survival of a subject, improve the subject's symptoms or condition, or both, by providing personalized treatment options. Thus, a subject suffering from a condition can have a biosignature generated from vesicles and other circulating biomarkers using one or more systems and methods disclosed herein, and the profile can then be used to determine whether a subject is a likely non-responder or responder to a particular treatment for the condition. Based on use of the biosignature to predict whether the subject is a non-responder or responder to the initially contemplated treatment, a particular treatment contemplated for treating the subject's condition can be selected for the subject, or another potentially more optimal treatment can be selected.

In one embodiment, a subject suffering from a condition is currently being treated with a therapeutic. A sample can be obtained from the subject before treatment and at one or more timepoints during treatment. A biosignature including vesicles or other biomarkers from the samples can be assessed and used to determine the subject's response to the drug, such as based on a change in the biosignature over time. If the subject is not responding to the treatment, e.g., the biosignature does not indicate that the patient is responding, the subject can be classified as being non-responsive to the treatment, or a non-responder. Similarly, one or more biomarkers associated with a worsening condition may be detected such that the biosignature is indicative of patient's failure to respond favorably to the treatment. In another example, one or more biomarkers associated with the condition remain the same despite treatment, indicating that the condition is not improving. Thus, based on the biosignature, a treatment regimen for the subject can be changed or adapted, including selection of a different therapeutic.

Alternatively, the subject can be determined to be responding to the treatment, and the subject can be classified as being responsive to the treatment, or a responder. For example, one or more biomarkers associated with an improvement in the condition or disorder may be detected. In another example, one or more biomarkers associated with the condition changes, thus indicating an improvement. Thus, the existing treatment can be continued. In another embodiment, even when there is an indiciation of improvement, the existing treatment may be adapted or changed if the biosignature indicates that another line of treatment may be more effective. The existing treatment may be combined with another therapeutic, the dosage of the current therapeutic may be increased, or a different candidate treatment or therapeutic may be selected. Criteria for selecting the different candidate treatment can depend on the setting. In one embodiment, the candidate treatment may have been known to be effective for subjects with success on the existing treatment. In another embodiment, the candidate treatment may have been known to be effective for other subjects with a similar biosignature.

In some embodiments, the subject is undergoing a second, third or more line of treatment, such as cancer treatment. A biosignature according to the invention can be determined for the subject prior to a second, third or more line of treatment, to determine whether a subject would be a responder or non-responder to the second, third or more line of treatment. In another embodiment, a biosignature is determined for the subject during the second, third or more line of treatment, to determine if the subject is responding to the second, third or more line of treatment.

The methods and systems described herein for assessing one or more vesicles can be used to determine if a subject suffering from a condition is responsive to a treatment, and thus can be used to select a treatment that improves one or more symptoms of the condition; decreases one or more side effects of an existing treatment; increases the improvement, or rate of improvement, in one or more symptoms as compared to a previous or other treatment; or prolongs survival as compared to without treatment or a previous or other treatment. Thus, the methods described herein can be used to prolong survival of a subject by providing personalized treatment options, and/or may reduce unnecessary treatment options and unnecessary side effects for a subject.

The prolonged survival can be an increased progression-free survival (PFS), which denotes the chances of staying free of disease progression for an individual or a group of individuals suffering from a disease, e.g., a cancer, after initiating a course of treatment. It can refer to the percentage of individuals in the group whose disease is likely to remain stable (e.g., not show signs of progression) after a specified duration of time. Progression-free survival rates are an indication of the effectiveness of a particular treatment. In other embodiments, the prolonged survival is disease-free survival (DFS), which denotes the chances of staying free of disease after initiating a particular treatment for an individual or a group of individuals suffering from a cancer. It can refer to the percentage of individuals in the group who are likely to be free of disease after a specified duration of time. Disease-free survival rates are an indication of the effectiveness of a particular treatment. Two treatment strategies can be compared on the basis of the disease-free survival that is achieved in similar groups of patients. Disease-free survival is often used with the term overall survival when cancer survival is described.

The candidate treatment selected by vesicle profiling as described herein can be compared to a non-vesicle profiling selected treatment by comparing the progression free survival (PFS) using therapy selected by vesicle profiling (period B) with PFS for the most recent therapy on which the subject has just progressed (period A). In one setting, a PFSB/PFSA ratio ≧1.3 is used to indicate that the vesicle profiling selected therapy provides benefit for subject (see for example, Robert Temple, Clinical measurement in drug evaluation. Edited by Wu Ningano and G. T. Thicker John Wiley and Sons Ltd. 1995; Von Hoff D. D. Clin Can Res. 4: 1079, 1999: Dhani et al. Clin Cancer Res. 15: 118-123, 2009).

Other methods of comparing the treatment selected by vesicle profiling can be compared to a non-vesicle profiling selected treatment by determine response rate (RECIST) and percent of subjects without progression or death at 4 months. The term “about” as used in the context of a numerical value for PFS means a variation of +/− ten percent (10%) relative to the numerical value. The PFS from a treatment selected by vesicle profiling can be extended by at least 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or at least 90% as compared to a non-vesicle profiling selected treatment. In some embodiments, the PFS from a treatment selected by vesicle profiling can be extended by at least 100%, 150%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900%, or at least about 1000% as compared to a non-vesicle profiling selected treatment. In yet other embodiments, the PFS ratio (PFS on vesicle profiling selected therapy or new treatment/PFS on prior therapy or treatment) is at least about 1.3. In yet other embodiments, the PFS ratio is at least about 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, or 2.0. In yet other embodiments, the PFS ratio is at least about 3, 4, 5, 6, 7, 8, 9 or 10.

Similarly, the DFS can be compared in subjects whose treatment is selected with or without determining a biosignature according to the invention. The DFS from a treatment selected by vesicle profiling can be extended by at least 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or at least 90% as compared to a non-vesicle profiling selected treatment. In some embodiments, the DFS from a treatment selected by vesicle profiling can be extended by at least 100%, 150%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900%, or at least about 1000% as compared to a non-vesicle profiling selected treatment. In yet other embodiments, the DFS ratio (DFS on vesicle profiling selected therapy or new treatment/DFS on prior therapy or treatment) is at least about 1.3. In yet other embodiments, the DFS ratio is at least about 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, or 2.0. In yet other embodiments, the DFS ratio is at least about 3, 4, 5, 6, 7, 8, 9 or 10.

In some embodiments, the candidate treatment selected by assessing circulating biomarkers does not increase the PFS ratio or the DFS ratio in the subject; nevertheless vesicle profiling provides subject benefit. For example, in some embodiments no known treatment is available for the subject. In such cases, vesicle profiling provides a method to identify a candidate treatment where none is currently identified. The vesicle profiling may extend PFS, DFS or lifespan by at least 1 week, 2 weeks, 3 weeks, 4 weeks, 1 month, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 2 months, 9 weeks, 10 weeks, 11 weeks, 12 weeks, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 12 months, 13 months, 14 months, 15 months, 16 months, 17 months, 18 months, 19 months, 20 months, 21 months, 22 months, 23 months, 24 months or 2 years. The vesicle profiling may extend PFS, DFS or lifespan by at least 2½ years, 3 years, 4 years, 5 years, or more. In some embodiments, the methods of the invention improve outcome so that subject is in remission.

The effectiveness of a treatment can be monitored by other measures. A complete response (CR) comprises a complete disappearance of the disease: no disease is evident on examination, scans or other tests. A partial response (PR) refers to some disease remaining in the body, but there has been a decrease in size or number of the lesions by 30% or more. Stable disease (SD) refers to a disease that has remained relatively unchanged in size and number of lesions. Generally, less than a 50% decrease or a slight increase in size would be described as stable disease. Progressive disease (PD) means that the disease has increased in size or number on treatment. In some embodiments, vesicle profiling according to the invention results in a complete response or partial response. In some embodiments, the methods of the invention result in stable disease. In some embodiments, the invention is able to achieve stable disease where non-vesicle profiling results in progressive disease.

In one embodiment, a method of determining sensitivity of a subject to a treatment is obtained by assessing a biomarker (such as the expression level of a biomarker, the mutation or other modification of a biomarker) from a subject and applying a model predictive of sensitivity to a treatment for cancer to the measurement. The model can be developed using an algorithm including without limitation linear sums, nearest neighbor, nearest centroid, linear discriminant analysis, support vector machines, and neural networks. By applying the model, the subject can be determined to be responsive or non-responsive to the treatment. Examples of such methods may include, but not be limited to those disclosed in PCT Publication No. WO2008138578, which is herein incorporated by reference in its entirety.

In one embodiment, the measurement is obtained by measuring the level of expression of one or more vesicle biomarkers as disclosed herein. In another embodiment, the method is performed in the presence of a second treatment. In another embodiment, the model combines the outcomes of linear sums, linear discriminant analysis, support vector machines, neural networks, k-nearest neighbors, and nearest centroids, or the model is cross-validated using a random sample of multiple measurements. In another embodiment, treatment, e.g., a compound, has previously failed to show efficacy in a patient. In another embodiment, the linear sum is compared to a sum of a reference population with known sensitivity; the sum of a reference population is the median of the sums derived from the population members' biomarker expression. In another embodiment, the model is derived from the components of a data set obtained by independent component analysis or is derived from the components of a data set obtained by principal component analysis.

The theranosis based on a biosignature of the invention can be for a phenotype including without limitation those listed herein. Characterizing a phenotype includes determining a theranosis for a subject, such as predicting whether a subject is likely to respond to a treatment (“responder”) or be non-responsive to a treatment (“non-responder”). As used herein, identifying a subject as a “responder” to a treatment or as a “non-responder” to the treatment comprises identifying the subject as either likely to respond to the treatment or likely to not respond to the treatment, respectively, and does not require determining a definitive prediction of the subject's response. One or more vesicles, or populations of vesicles, obtained from subject are used to determine if a subject is a non-responder or responder to a particular therapeutic, by assessing biomarkers disclosed herein, e.g., those listed in Table 10. Detection of a high or low expression level of a biomarker, or a mutation of a biomarker, can be used to select a candidate treatment, such as a pharmaceutical intervention, for a subject with a condtion. Table 10 contains illustrative conditions and pharmaceutical interventions for those conditions. The table lists biomarkers that affect the efficacy of the intervention. The biomarkers can be assessed using the methods of the invention, e.g., as circulating biomarkers or in association with a vesicle.

TABLE 10 Examples of Biomarkers and Pharmaceutical Intervention for a Condition Condition Pharmaceutical intervention Biomarker Peripheral Arterial Disease Atorvastatin C-reactive protein(CRP) Simvastatin serum Amylyoid A (SAA) Rosuvastatin interleukin-6 Pravastatin intracellular adhesion molecule Fluvastatin (ICAM) Lovastatin vascular adhesion molecule (VCAM) CD40L fibrinogen fibrin D-dimer fibrinopeptide A von Willibrand factor tissue plasminogen activator antigen (t-PA) factor VII prothrombin fragment 1 oxidized low density lipoprotein (oxLDL) lipoprotein A Non-Small Cell Lung Cancer Erlotinib EGFR Carboplatin excision repair cross- Paclitaxel complementation group 1 (ERCC1) Gefitinib p53 Ras p27 class III beta tubulin breast cancer gene 1 (BRCA1) breast cancer gene 1 (BRCA2) ribonucleotide reductase messenger 1 (RRM1) Colorectal Cancer Panitumumab K-ras Cetuximab Breast Cancer Trastuzumab HER2 Anthracyclines toposiomerase IIalpha Taxane estrogen receptor Methotrexate progesterone receptor fluorouracil Alzheimer's Disease Donepezil beta-amyloid protein Galantamine amyloid precursor protein (APP) Memantine APP670/671 Rivastigmine APP693 Tacrine APP692 APP715 APP716 APP717 APP723 presenilin 1 presenilin 2 cerebrospinal fluid amyloid beta protein 42 (CSF-Abeta42) cerebrospinal fluid amyloid beta protein 40 (CSF-Abeta40) F2 isoprostane 4-hydroxynonenal F4 neuroprostane acrolein Arrhythmia Disopyramide SERCA Flecainide AAP Lidocaine Connexin 40 Mexiletine Connexin 43 Moricizine ATP-sensitive potassium channel Procainamide Kv1.5 channel Propafenone acetylcholine-activated posassium Quinidine channel Tocainide Acebutolol Atenolol Betaxolol Bisoprolol Carvedilol Esmolol Metoprolol Nadolol Propranolol Sotalol Timolol Amiodarone Azimilide Bepridil Dofetilide Ibutilide Tedisamil Diltiazem Verapamil Azimilide Dronedarone Amiodarone PM101 ATI-2042 Tedisamil Nifekalant Ambasilide Ersentilide Trecetilide Almokalant D-sotalol BRL-32872 HMR1556 L768673 Vernakalant AZD70009 AVE0118 S9947 NIP-141/142 XEN-D0101/2 Ranolazine Pilsicainide JTV519 Rotigaptide GAP-134 Rheumatoid arthritis Methotrexate 677CC/1298AA MTHFR infliximab 677CT/1298AC MTHFR adalimumab 677CT MTHFR etanercept G80AA RFC-1 sulfasalazine 3435TT MDR1 (ABCB1) 3435TT ABCB1 AMPD1/ATIC/ITPA IL1-RN3 HLA-DRB103 CRP HLA-D4 HLA DRB-1 anti-citrulline epitope containing peptides anti-A1/RA33 Erythrocyte sedimentation rate (ESR) C-reactive protein (CRP) SAA (serum amyloid-associated protein) rheumatoid factor IL-1 TNF IL-6 IL-8 IL-1Ra Hyaluronic acid Aggrecan Glc-Gal-PYD osteoprotegerin RNAKL carilage oligomeric matrix protein (COMP) calprotectin Arterial Fibrillation warfarin F1.2 aspirin TAT anticoagulants FPA heparin beta-throboglobulin ximelagatran platelet factor 4 soluble P-selectin IL-6 CRP HIV Infection Zidovudine HIV p24 antigen Didanosine TNF-alpha Zalcitabine TNFR-II Stavudine CD3 Lamivudine CD14 Saquinavir CD25 Ritonavir CD27 Indinavir Fas Nevirane FasL Nelfinavir beta2 microglobulin Delavirdine neopterin Stavudine HIV RNA Efavirenz HLA-B *5701 Etravirine Enfuvirtide Darunavir Abacavir Amprenavir Lonavir/Ritonavirc Tenofovir Tipranavir Cardiovascular Disease lisinopril ACE inhibitor candesartan angiotensin enalapril

Cancer

Vesicle biosignatures can be used in the theranosis of a cancer, such as identifying whether a subject suffering from cancer is a likely responder or non-responder to a particular cancer treatment. The subject methods can be used to theranose cancers including those listed herein, e.g., in the “Phenotype” section above. These include without limitation lung cancer, non-small cell lung cancerm small cell lung cancer (including small cell carcinoma (oat cell cancer), mixed small cell/large cell carcinoma, and combined small cell carcinoma), colon cancer, breast cancer, prostate cancer, liver cancer, pancreatic cancer, brain cancer, kidney cancer, ovarian cancer, stomach cancer, melanoma, bone cancer, gastric cancer, breast cancer, glioma, gliobastoma, hepatocellular carcinoma, papillary renal carcinoma, head and neck squamous cell carcinoma, leukemia, lymphoma, myeloma, or other solid tumors.

Cancer: Biosignatures

A biosignature can be determined to provide a theranosis for a subject. The biosignature of a vesicle can comprise one or more biomarkers such as, but not limited to, any one or more biomarkers as described herein, such as, but not limited to, those listed in FIGS. 1-60, or Tables 3-10, 12-14, 22, 26, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof.

The invention provides numerous methods of identifying a biosignature for characterizing a cancer. Further provided herein are biomarkers that are assessed to identify the biosignature. In one embodiment, a biosignature for prostate cancer comprises one or more of the following biomarkers: EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP, and EGFR. In another embodiment, a biosignature for classifying a prostate cancer as being castration-resistant comprises EpCam+, CK+, CD45-vesicles. In another embodiment, a vesicle biosignature for theranosing small cell lung cancer comprises miR-451, miR-92a-2*, miR-147, and/or miR-574-5p. In yet another embodiment, a biosignature for the theranosis of colorectal cancer comprises one or more miRs selected from the group consisting of: miR-548c-5p, miR-362-3p, miR-422a, miR-597, miR-429, miR-200a, and miR-200b. The biosignature for theranosing a cancer can comprise one or more of CA IX, CMET, VEGFR2, VEGF, Vimentin, CD44v6, Ckit, Ax1, RET (ret proto-oncogene), E Cadherin, and VE Cadherin.

Cancer: Standard of Care

A biosignature of circulating biomarkers, including markers associated with vesicle, in a sample from a subject suffering from a cancer can be used select a candidate treatment for the subject. The biosignature can be determined according to the methods of the invention presented herein. In some embodiments, the candidate treatment comprises a standard of care for the cancer. The biosignature can be used to determine if a subject is a non-responder or responder to a particular treatment or standard of care. The treatment can be a cancer treatment such as radiation, surgery, chemotherapy or a combination thereof. The cancer treatment can be a therapeutic such as anti-cancer agents and chemotherapeutic regimens. Cancer treatments for use with the methods of the invention include without limitation those listed in Table 11:

TABLE 11 Cancer Treatments Treatment or Agent Cancer therapies Radiation, Surgery, Chemotherapy, Biologic therapy, Neo-adjuvant therapy, Adjuvant therapy, Palliative therapy, Watchful waiting Anti-cancer agents 13-cis-Retinoic Acid, 2-CdA, 2-Chlorodeoxyadenosine, 5-Azacitidine, 5-Fluorouracil, (chemotherapies and 5-FU, 6-Mercaptopurine, 6-MP, 6-TG, 6-Thioguanine, Abraxane, Accutane ®, biologics) Actinomycin-D, Adriamycin ®, Adrucil ®, Afinitor ®, Agrylin ®, Ala-Cort ®, Aldesleukin, Alemtuzumab, ALIMTA, Alitretinoin, Alkaban-AQ ®, Alkeran ®, All- transretinoic Acid, Alpha Interferon, Altretamine, Amethopterin, Amifostine, Aminoglutethimide, Anagrelide, Anandron ®, Anastrozole, Arabinosylcytosine, Ara-C, Aranesp ®, Aredia ®, Arimidex ®, Aromasin ®, Arranon ®, Arsenic Trioxide, Asparaginase, ATRA, Avastin ®, Azacitidine, BCG, BCNU, Bendamustine, Bevacizumab, Bexarotene, BEXXAR ®, Bicalutamide, BiCNU, Blenoxane ®, Bleomycin, Bortezomib, Busulfan, Busulfex ®, C225, Calcium Leucovorin, Campath ®, Camptosar ®, Camptothecin-11, Capecitabine, Carac ™, Carboplatin, Carmustine, Carmustine Wafer, Casodex ®, CC-5013, CCI-779, CCNU, CDDP, CeeNU, Cerubidine ®, Cetuximab, Chlorambucil, Cisplatin, Citrovorum Factor, Cladribine, Cortisone, Cosmegen ®, CPT-11, Cyclophosphamide, Cytadren ®, Cytarabine, Cytarabine Liposomal, Cytosar-U ®, Cytoxan ®, Dacarbazine, Dacogen, Dactinomycin, Darbepoetin Alfa, Dasatinib, Daunomycin Daunorubicin, Daunorubicin Hydrochloride, Daunorubicin Liposomal, DaunoXome ®, Decadron, Decitabine, Delta-Cortef ®, Deltasone ®, Denileukin, Diftitox, DepoCyt ™, Dexamethasone, Dexamethasone Acetate Dexamethasone Sodium Phosphate, Dexasone, Dexrazoxane, DHAD, DIC, Diodex Docetaxel, Doxil ®, Doxorubicin, Doxorubicin Liposomal, Droxia ™, DTIC, DTIC- Dome ®, Duralone ®, Efudex ®, Eligard ™, Ellence ™, Eloxatin ™, Elspar ®, Emcyt ®, Epirubicin, Epoetin Alfa, Erbitux, Erlotinib, Erwinia L-asparaginase, Estramustine, Ethyol Etopophos ®, Etoposide, Etoposide Phosphate, Eulexin ®, Everolimus, Evista ®, Exemestane, Fareston ®, Faslodex ®, Femara ®, Filgrastim, Floxuridine, Fludara ®, Fludarabine, Fluoroplex ®, Fluorouracil, Fluorouracil (cream), Fluoxymesterone, Flutamide, Folinic Acid, FUDR ®, Fulvestrant, G-CSF, Gefitinib, Gemcitabine, Gemtuzumab ozogamicin, Gemzar, Gleevec ™, Gliadel ® Wafer, GM-CSF, Goserelin, Granulocyte - Colony Stimulating Factor, Granulocyte Macrophage Colony Stimulating Factor, Halotestin ®, Herceptin ®, Hexadrol, Hexalen ®, Hexamethylmelamine, HMM, Hycamtin ®, Hydrea ®, Hydrocort Acetate ®, Hydrocortisone, Hydrocortisone Sodium Phosphate, Hydrocortisone Sodium Succinate, Hydrocortone Phosphate, Hydroxyurea, Ibritumomab, Ibritumomab, Tiuxetan, Idamycin ®, Idarubicin, Ifex ®, IFN-alpha, Ifosfamide, IL-11, IL-2, Imatinib mesylate, Imidazole Carboxamide, Interferon alfa, Interferon Alfa-2b (PEG Conjugate), Interleukin-2, Interleukin-11, Intron A ® (interferon alfa-2b), Iressa ®, Irinotecan, Isotretinoin, Ixabepilone, Ixempra ™, Kidrolase (t), Lanacort ®, Lapatinib, L-asparaginase, LCR, Lenalidomide, Letrozole, Leucovorin, Leukeran, Leukine ™, Leuprolide, Leurocristine, Leustatin ™, Liposomal Ara-C Liquid Pred ®, Lomustine, L-PAM, L-Sarcolysin, Lupron ®, Lupron Depot ®, Matulane ®, Maxidex, Mechlorethamine, Mechlorethamine Hydrochloride, Medralone ®, Medrol ®, Megace ®, Megestrol, Megestrol Acetate, Melphalan, Mercaptopurine, Mesna, Mesnex ™, Methotrexate, Methotrexate Sodium, Methylprednisolone, Meticorten ®, Mitomycin, Mitomycin-C, Mitoxantrone, M-Prednisol ®, MTC, MTX, Mustargen ®, Mustine, Mutamycin ®, Myleran ®, Mylocel ™, Mylotarg ®, Navelbine ®, Nelarabine, Neosar ®, Neulasta ™, Neumega ®, Neupogen ®, Nexavar ®, Nilandron ®, Nilutamide, Nipent ®, Nitrogen Mustard, Novaldex ®, Novantrone ®, Octreotide, Octreotide acetate, Oncospar ®, Oncovin ®, Ontak ®, Onxal ™, Oprevelkin, Orapred ®, Orasone ®, Oxaliplatin, Paclitaxel, Paclitaxel Protein-bound, Pamidronate, Panitumumab, Panretin ®, Paraplatin ®, Pediapred ®, PEG Interferon, Pegaspargase, Pegfilgrastim, PEG-INTRON ™, PEG-L-asparaginase, PEMETREXED, Pentostatin, Phenylalanine Mustard, Platinol ®, Platinol-AQ ®, Prednisolone, Prednisone, Prelone ®, Procarbazine, PROCRIT ®, Proleukin ®, Prolifeprospan 20 with Carmustine Implant, Purinethol ®, Raloxifene, Revlimid ®, Rheumatrex ®, Rituxan ®, Rituximab, Roferon-A ® (Interferon Alfa-2a), Rubex ®, Rubidomycin hydrochloride, Sandostatin ®, Sandostatin LAR ®, Sargramostim, Solu-Cortef ®, Solu-Medrol ®, Sorafenib, SPRYCEL ™, STI-571, Streptozocin, SU11248, Sunitinib, Sutent ®, Tamoxifen, Tarceva ®, Targretin ®, Taxol ®, Taxotere ®, Temodar ®, Temozolomide, Temsirolimus, Teniposide, TESPA, Thalidomide, Thalomid ®, TheraCys ®, Thioguanine, Thioguanine Tabloid ®, Thiophosphoamide, Thioplex ®, Thiotepa, TICE ®, Toposar ®, Topotecan, Toremifene, Torisel ®, Tositumomab, Trastuzumab, Treanda ®, Tretinoin, Trexall ™, Trisenox ®, TSPA, TYKERB ®, VCR, Vectibix ™, Velban ®, Velcade ®, VePesid ®, Vesanoid ®, Viadur ™, Vidaza ®, Vinblastine, Vinblastine Sulfate, Vincasar Pfs ®, Vincristine, Vinorelbine, Vinorelbine tartrate, VLB, VM-26, Vorinostat, VP-16, Vumon ®, Xeloda ®, Zanosar ®, Zevalin ™, Zinecard ®, Zoladex ®, Zoledronic acid, Zolinza, Zometa ® Combination CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone); CVP Therapies (cyclophosphamide, vincristine, and prednisone); RCVP (Rituximab + CVP); RCHOP (Rituximab + CHOP); RICE (Rituximab + ifosamide, carboplatin, etoposide); RDHAP, (Rituximab + dexamethasone, cytarabine, cisplatin); RESHAP (Rituximab + etoposide, methylprednisolone, cytarabine, cisplatin); combination treatment with vincristine, prednisone, and anthracycline, with or without asparaginase; combination treatment with daunorubicin, vincristine, prednisone, and asparaginase; combination treatment with teniposide and Ara-C (cytarabine); combination treatment with methotrexate and leucovorin; combination treatment with bleomycin, doxorubicin, etoposide, mechlorethamine, prednisone, vinblastine, and vincristine; FOLFOX4 regimen (oxaliplatin, leucovorin, and fluorouracil [5-FU]); FOLFIRI regimen (Irinotecan Hydrochloride, Fluorouracil, and Leucovorin Calcium); Levamisole regimen (5-FU and levamisole); NCCTG regimen (5-FU and low-dose leucovorin); NSABP regimen (5-FU and high-dose leucovorin); XAD (Xelox (Capecitabine + Oxaliplatin) + Bevacizumab + Dasatinib); FOLFOX/Bevacizumab/Hydroxychloroquine; German AIO regimen (folic acid, 5-FU, and irinotecan); Douillard regimen (folic acid, 5-FU, and irinotecan); CAPDX regimen (Capecitabine, oxaliplatin); FOLFOX6 regimen (oxaliplatin, leucovorin, and 5-FU); FOLFIRI regimen (folic acid, 5-FU, and irinotecan); FUFOX regimen (oxaliplatin, leucovorin, and 5-FU); FUOX regimen (oxaliplatin and 5-FU); IFL regimen (irinotecan, 5-FU, and leucovorin); XELOX regimen (capecitabine oxaliplatin); KHAD-L (ketoconazole, hydrocortisone, dutasteride and lapatinib); Biologics anti-CD52 antibodies (e.g., Alemtuzumab), anti-CD20 antibodies (e.g., Rituximab), anti-CD40 antibodies (e.g., SGN40) Classes of Anthracyclines and related substances, Anti-androgens, Anti-estrogens, Antigrowth Treatments hormones (e.g., Somatostatin analogs), Combination therapy (e.g., vincristine, bcnu, melphalan, cyclophosphamide, prednisone (VBMCP)), DNA methyltransferase inhibitors, Endocrine therapy - Enzyme inhibitor, Endocrine therapy - other hormone antagonists and related agents, Folic acid analogs (e.g., methotrexate), Folic acid analogs (e.g., pemetrexed), Gonadotropin releasing hormone analogs, Gonadotropin- releasing hormones, Monoclonal antibodies (EGFR-Targeted - e.g., panitumumab, cetuximab), Monoclonal antibodies (Her2-Targeted - e.g., trastuzumab), Monoclonal antibodies (Multi-Targeted - e.g., alemtuzumab), Other alkylating agents, Antineoplastic agents (e.g., asparaginase, ATRA, bexarotene, celecoxib, gemcitabine, hydroxyurea, irinotecan, topotecan, pentostatin), Cytotoxic antibiotics, Platinum compounds, Podophyllotoxin derivatives (e.g., etoposide), Progestogens, Protein kinase inhibitors (EGFR-Targeted), Protein kinase inhibitors (Her2 targeted therapy - e.g., lapatinib), Pyrimidine analogs (e.g., cytarabine), Pyrimidine analogs (e.g., fluoropyrimidines), Salicylic acid and derivatives (e.g., aspirin), Src-family protein tyrosine kinase inhibitors (e.g., dasatinib), Taxanes (e.g., nab-paclitaxel), Vinca Alkaloids and analogs, Vitamin D and analogs, Monoclonal antibodies (Multi-Targeted - e.g., bevacizumab), Protein kinase inhibitors (e.g., imatinib, sorafenib, sunitinib) Prostate Cancer Watchful waiting (i.e., monitor without treatment); Surgery (e.g., Pelvic Treatments lymphadenectomy, Radical prostatectomy, Transurethral resection of the prostate (TURP); Orchiectomy); Radiation therapy (e.g., external-beam radiation therapy (EBRT), Proton beam radiation; implantation of radioisotopes (i.e., iodine I 125, palladium, and iridium)); Hormone therapy (e.g., Luteinizing hormone-releasing hormone agonists such as leuprolide, goserelin, buserelin or ozarelix; Antiandrogens such as flutamide, 2-hydroxyflutamide, bicalutamide, megestrol acetate, nilutamide, ketoconazole, aminoglutethimide; calcitriol, gonadotropin-releasing hormone (GnRH), estrogens (DES, chlorotrianisene, ethinyl estradiol, conjugated estrogens USP, and DES- diphosphate), triptorelin, finasteride, cyproterone acetate, ASP3550); Cryosurgery/cryotherapy; Chemotherapy and Biologic therapy (dutasteride, zoledronate, azacitidine, docetaxel, prednisolone, celecoxib, atorvastatin, AMT2003, soy protein, LHRH agonist, PD-103, pomegranate extract, soy extract, taxotere, I-125, zoledronic acid, dasatinib, vitamin C, vitamin D, vitamin D3, vitamin E, gemcitabine, cisplatin, lenalidomide, prednisone, degarelix, OGX-011, OGX-427, MDV3100, tasquinimod, cabazitaxel, TOOKAD ®, lanreotide, PROSTVAC, GM-CSF, lenalidomide, samarium Sm-153 lexidronam, N-Methyl-D-Aspartate (NMDA)-Receptor Antagonist, sorafenib, sorafenib tosylate, mitoxantrone, ABI-008, hydrocortisone, panobinostat, soy-tomato extract, KHAD-L, TOK-001, cixutumumab, temsirolimus, ixabepilone, TAK-700, TAK-448, TRC105, cyclophosphamide, lenalidomide, MLN8237, GDC-0449, Alpharadin ®, ARN-509, PX-866, ISIS EIF4E Rx, AEZS-108, 131I-F16SIP Monoclonal Antibody, anti-OX40 antibody, Muscadine Plus, ODM-201, BBI608, ZD4054, erlotinib, rIL-2, epirubicin, estramustine phosphate, HuJ591-GS monoclonal (177Lu-J591), abraxane, IVIG, fermented wheat germ nutriment (FWGE), 153Sm-EDTMP, estramustine, mitoxantrone, vinblastine, carboplatin, paclitaxel, pazopanib, cytarabine, testosterone replacement, Zoledronic Acid, Strontium Chloride Sr 89, paricalcitol, satraplatin, RAD001 (everolimus), valproic acid, tea extract, Hamsa-1, hydroxychloroquine, sipuleucel-T, selenomethionine, selenium, lycopene, sunitinib, vandetanib, IMC-A12 antibody, monoclonal antibody IMC-3G3, ixabepilone, diindolylmethane, metformin, efavirenz, dasatinib, nilutamide, abiraterone, cabozantinib (XL184), isoflavines, cinacalcet hydrochloride, SB939, LY2523355, KX2-391, olaparib, genestein, digoxin, RO4929097, ipilimumab, bafetinib, cediranib maleate, MK2206, phenelzine sulfate, triptorelin pamoate, saracatinib, STA-9090, tesetaxel, pasireotide, afatinib, GTx 758, lonafarnib, satraplatin, radiolabeled antibody 7E11, FP253/fludarabine, Coxsackie A21 (CVA21) virus, ARRY-380, ARRY-382, anti- PSMA designer T cells, pemetrexed disodium, bortezomib, MDX-1106, white button mushroom extract, SU011248, MLN9708, BMTP-11, ABT-888, CX-4945, 4SC-205, temozolomide, MGAH22, vinorelbine ditartrate, Sodium Selenite, vorinostat, Ad- REIC/Dkk-3, ASG-5ME, IMF-001, PROHIBITIN-TP01, DSTP3086S, ridaforolimus, MK-2206, MK-0752, polyunsaturated fatty acids, I-125, statins, cholecalciferol, omega- 3 fatty acids, raloxifene, etoposide, POMELLA ™ extract, Lucrin depot); Cancer vaccines (e.g., DNA vaccines, peptide vaccines, dendritic cell vaccines, PEP223, PSA/TRICOM, PROSTVAC-V/TRICOM, PROSTVAC-F/TRICOM, PSA vaccine, TroVax ®, GI-6207, PSMA and TARP Peptide Vaccine); Ultrasound; Proton beam radiation Colorectal Cancer Primary Surgical Therapy (e.g., local excision; resection and anastomosis of primary Treatments lesion and removal of surrounding lymph nodes); Adjuvant Therapy (e.g., fluorouracil (5-FU), capecitabine, leucovorin, oxaliplatin, erlotinib, irinotecan, aspirin, mitomycin C, suntinib, cetuximab, bevacizumab, pegfilgrastim, panitumumab, ramucirumab, curcumin, celecoxib, FOLFOX4 regimen, FOLFOX6 regimen, FOLFIRI regimen, FUFOX regimen, FUOX regimen, IFL regimen, XELOX regimen, 5-FU and levamisole regimens, German AIO regimen, CAPOX regimen, Douillard regimen, XAD, RAD001 (everolimus), ARQ 197, BMS-908662, JI-101, hydroxychloroquine (HCQ), Yttrium Microspheres, EZN-2208, CS-7017, IMC-1121B, IMC-18F1, docetaxel, lonafarnib, Maytansinoid DM4-Conjugated Humanized Monoclonal Antibody huC242, paclitaxel, ARRY-380, ARRY-382, IMO-2055, MDX1105-01, CX-4945, Pazopanib, Ixabepilone, OSI-906, NPC-1C Chimeric Monoclonal Antibody, brivanib, Poly-ADP Ribose (PARP) Inhibitor, RO4929097, Anti-cancer vaccine, CEA vaccine, cyclophosphamide, yttrium Y 90 DOTA anti-CEA monoclonal antibody M5A, MEHD7945A, ABT-806, ABT-888, MEDI-565, LY2801653, AZD6244, PRI-724, BKM120, tivozanib, floxuridine, dexamethosone, NKTR-102, perifosine, regorafenib, EP0906, Celebrex, PHY906, KRN330, imatinib mesylate, azacitidine, entinostat, PX-866, ABX-EGF, BAY 43-9006, ESO-1 Lymphocytes and Aldesleukin, LBH589, olaparib, fostamatinib, PD 0332991, STA-9090, cholecalciferol, GI-4000, IL-12, AMG 706, temsirolimus, dulanermin, bortezomib, ursodiol, ridaforolimus, veliparib, NK012, Dalotuzumab, MK-2206, MK- 0752, lenalidomide, REOLYSIN ®, AUY922, PRI-724, BKM120, avastin, dasatinib); Adjuvant Radiation Therapy (particularly for rectal cancer)

As shown in Table 11, cancer treatments include various surgical and therapeutic treatments. Anti-cancer agents include drugs such as small molecules and biologicals. The methods of the invention can be used to identify a biosignature comprising circulating biomarkers that can then be used for theranostic purposes such as monitoring a treatment efficacy, classifying a subject as a responder or non-responder to a treatment, or selecting a candidate therapeutic agent. The invention can be used to provide a theranosis for any cancer treatments, including without limitation thernosis involving the cancer treatments in Tables 11-13. Cancer therapies that can be identified as candidate treatments by the methods of the invention include without limitation the chemotherapeutic agents listed in Tables 11-13 and any appropriate combinations thereof. In one embodiment, the treatments are specific for a specific type of cancer, such as the treatments listed for prostate cancer, colorectal cancer, breast cancer and lung cancer in Table 11. In other embodiments, the treatments are specific for a tumor regardless of its origin but that displays a certain biosignature, such as a biosignature comprising a marker listed in Tables 10, 12-13, or a treatment associated marker listed in Table 14.

The invention provides methods of monitoring a cancer treatment comprising identifying a series of biosignatures in a subject over a time course, such as before and after a treatment, or over time after the treatment. The biosignatures are compared to a reference to determine the efficacy of the treatment. In an embodiment, the treatment is selected from Tables 11-13, such as radiation, surgery, chemotherapy, biologic therapy, neo-adjuvant therapy, adjuvant therapy, or watchful waiting. The reference can be from another individual or group of individuals or from the same subject. For example, a subject with a biosignature indicative of a cancer pre-treatment may have a biosignature indicative of a healthy state after a successful treatment. Conversely, the subject may have a biosignature indicative of cancer after an unsuccessful treatment. The biosignatures can be compared over time to determine whether the subject's biosignatures indicate an improvement, worsening of the condition, or no change. Additional treatments may be called for if the cancer is worsening or there is no change over time. For example, hormone therapy may be used in addition to surgery or radiation therapy to treat more aggressive prostate cancers. One or more of the following miRs can be used in a biosignature for monitoring an efficacy of prostate cancer treatment: hsa-miR-1974, hsa-miR-27b, hsa-miR-103, hsa-miR-146a, hsa-miR-22, hsa-miR-382, hsa-miR-23a, hsa-miR-376c, hsa-miR-335, hsa-miR-142-5p, hsa-miR-221, hsa-miR-142-3p, hsa-miR-151-3p, hsa-miR-21, hsa-miR-16. One or more miRs listed in the following publication can be used in a biosignature for monitoring treatment of a cancer of the GI tract: Albulescu et al., Tissular and soluble miRNAs for diagnostic and therapy improvement in digestive tract cancers, Exp Rev Mol Diag, 11:1, 101-120.

In some embodiments, the invention provides a method of identifying a biosignature in a sample from a subject in order to select a candidate therapeutic. For example, the biosignature may indicate that a drug-associated target is mutated or differentially expressed, thereby indicating that the subject is likely to respond or not respond to certain treatments. The candidate treatments can be chosen from the anti-cancer agents or classes of therapeutic agents identified in Tables 11-13. In some embodiments, the candidate treatments identified according to the subject methods are chosen from at least the groups of treatments consisting of 5-fluorouracil, abarelix, alemtuzumab, aminoglutethimide, anastrozole, asparaginase, aspirin, ATRA, azacitidine, bevacizumab, bexarotene, bicalutamide, calcitriol, capecitabine, carboplatin, celecoxib, cetuximab, chemotherapy, cholecalciferol, cisplatin, cytarabine, dasatinib, daunorubicin, decitabine, doxorubicin, epirubicin, erlotinib, etoposide, exemestane, flutamide, fulvestrant, gefitinib, gemcitabine, gonadorelin, goserelin, hydroxyurea, imatinib, irinotecan, lapatinib, letrozole, leuprolide, liposomal-doxorubicin, medroxyprogesterone, megestrol, megestrol acetate, methotrexate, mitomycin, nab-paclitaxel, octreotide, oxaliplatin, paclitaxel, panitumumab, pegaspargase, pemetrexed, pentostatin, sorafenib, sunitinib, tamoxifen, taxanes, temozolomide, toremifene, trastuzumab, VBMCP, and vincristine.

Similar to selecting a candidate treatment, the invention also provides a method of determining whether to treat a cancer at all. For example, prostate cancer can be a non-aggressive disease that is unlikely to substantially harm the subject. Radiation therapy with androgen ablation (hormone reduction) is the standard method of treating locally advanced prostate cancer. Morbidities of hormone therapy include impotence, hot flashes, and loss of libido. In addition, a treatment such as prostatectomy can have morbidities such as impotence or incontinence. Therefore, the invention provides biosignatures that indicate aggressiveness or a progression (e.g., stage or grade) of the cancer. A non-aggressive cancer or localized cancer might not require immediate treatment but rather be watched, e.g., “watchful waiting” of a prostate cancer. Whereas an aggressive or advanced stage lesion would require a concomitantly more aggressive treatment regimen.

Examples of biomarkers that can be detected, and treatment agents that can be selected or possibly avoided are listed in Tables 12-13. For example, a biosignature is identified for a subject with a prostate cancer, wherein the biosignature comprises levels of androgen receptor (AR). Overexpression or overproduction of AR, such as high levels of mRNA levels or protein levels in a vesicle, provides an identification of candidate treatments for the subject. Such treatments include agents for treating the subject such as Bicalutamide, Flutamide, Leuprolide, or Goserelin. The subject is accordingly identified as a responder to Bicalutamide, Flutamide, Leuprolide, or Goserelin. In another illustrative example, BCRP mRNA, protein, or both is detected at high levels in a vesicle from a subject suffering from NSCLC. The subject may then be classified as a non-responder to the agents Cisplatin and Carboplatin, or the agents are considered to be less effective than other agents for treating NSCLC in the subject and not selected for use in treating the subject. Any of the following biomarkers can be assessed in a vesicle obtained from a subject, and the biomarker can be in the form including but not limited to one or more of a nucleic acid, polypeptide, peptide or peptide mimetic. In yet another illustrative example, a mutation in one or more of KRAS, BRAF, PIK3CA, and/or c-kit can be used to select a candidate treatment. For example, a mutation in KRAS or BRAF in a patient may indicate that cetuximab and/or panitumumab are likely to be less effective in treating the patient.

TABLE 12 Examples of Biomarkers, Lineage and Agents Possibly Less Effective Possible Agents to Biomarker Lineage Agents Consider AR (high expression) Prostate Bicalutamide, Flutamide, Leuprolide, Goserelin AR (high expression) default Bicaluamide, Flutamide, Leuprolide, Goserelin BCRP (high Non-small cell lung cancer Cisplatin, Carboplatin expression) (NSCLC) BCRP (low Non-small cell lung cancer Cisplatin, Carboplatin expression) (NSCLC) BCRP (high default Cisplatin, Carboplatin expression) BCRP (low default Cisplatin, Carboplatin expression) BRAF V600E Colorectal Cetuximab, Panitumumab (mutation positive) BRAF V600E Colorectal Cetuximab, Panitumumab (mutation negative) BRAF V600E All other Cetuximab, Panitumumab (mutation positive) BRAF V600E All other Cetuximab, Panitumumab (mutation negative) BRAF V600E default Cetuximab, Panitumumab (mutation positive) BRAF V600E default Cetuximab, Panitumumab (mutation negative) CD52 (high Leukemia Alemtuzumab expression) CD52 (low Leukemia Alemtuzumab expression) CD52 (high default (Hematologic Alemtuzumab expression) malignancies only) CD52 (low default (Hematologic Alemtuzumab expression) malignancies only) c-kit Uveal Melanoma c-kit (high expression) Gastrointestinal Stromal Imatinib Tumors [GIST]; cKIT will not be performed on Uveal Melanoma as imatinib is not useful in the setting of WT cKIT positive uveal melanoma (see Hofmann et al. 2009) c-kit (high expression) Extrahepatic Bile Duct Imatinib Tumors; cKIT will not be performed on Uveal Melanoma as imatinib is not useful in the setting of WT cKIT positive uveal melanoma (see Hofmann et al. 2009) c-kit (high expression) Acute myeloid leukemia Imatinib (AML) c-kit (high expression) default; cKIT will not be Imatinib performed on Uveal Melanoma as imatinib is not useful in the setting of WT cKIT positive uveal melanoma (see Hofmann et al. 2009) EGFR (high copy Head and neck squamous Erlotinib, Gefitinib number) cell carcinoma (HNSCC) EGFR Head and neck squamous Erlotinib, Gefitinib cell carcinoma (HNSCC) EGFR (high copy Non-small cell lung cancer Erlotinib, Gefitinib number) (NSCLC) EGFR (low copy Non-small cell lung cancer Erlotinib, Gefitinib number) (NSCLC) EGFR (high copy default Cetuxumab, Panitumumab, number) Erlotinib, Gefitinib EGFR (low copy default Cetuxumab, Panitumumab, number) Erlotinib, Gefitinib ER (high expression) Breast Ixabepilone Tamoxifen-based treatment, aromatase inhibitors (anastrazole, letrozole) ER (low expression) Breast Ixabepilone ER (high expression) Ovarian Tamoxifen-based treatment, aromatase inhibitors (anastrazole, letrozole) ER (high expression) default Tamoxifen-based treatment, aromatase inhibitors (anastrazole, letrozole) ERCC1 (high Non-small cell lung cancer Carboplatin, Cisplatin expression) (NSCLC) ERCC1 (low Non-small cell lung cancer Carboplatin, Cisplatin expression) (NSCLC) ERCC1 (high Small Cell Lung Cancer Carboplatin, Cisplatin expression) (SCLC) ERCC1 (low Small Cell Lung Cancer Carboplatin, Cisplatin expression) (SCLC) ERCC1 (high Gastric Oxaliplatin expression) ERCC1 (low Gastric Oxaliplatin expression) ERCC1 (high default Carboplatin, Cisplatin, expression) Oxaliplatin ERCC1 (low default Carboplatin, Cisplatin, expression) Oxaliplatin HER-2 (high Breast Lapatinib, Trastuzumab expression) HER-2 (high default Lapatinib, Trastuzumab expression) KRAS (mutation Colorectal cancer Cetuximab, Panitumumab positive) KRAS (mutation Colorectal cancer Cetuximab, Panitumumab negative) KRAS (mutation Non-small cell lung cancer Erlotinib, Gefitinib positive) (NSCLC) KRAS (mutation Non-small cell lung cancer Erlotinib, Gefitinib negative) (NSCLC) KRAS (mutation Bronchioloalveolar Erlotinib positive) carcinoma (BAC) or adenocarcinoma (BAC subtype) KRAS (mutation Bronchioloalveolar Erlotinib negative) carcinoma (BAC) or adenocarcinoma (BAC subtype) KRAS (mutation Multiple myeloma VBMCP/Cyclophosphamides positive) KRAS (mutation Multiple myeloma VBMCP/Cyclophosphamide negative) KRAS (mutation default Cetuximab, Panitumumab positive) KRAS (mutation default Cetuximab, panitumumab negative) KRAS (mutation default Cetuximab, Erlotinib, positive) Panitumumab, Gefitinib KRAS (mutation default Cetuximab, Erlotinib, negative) Panitumumab, Gefitinib MGMT (high Pituitary tumors, Temozolomide expression) oligodendroglioma MGMT (low Pituitary tumors, Temozolomide expression) oligodendroglioma MGMT (high Neuroendocrine tumors Temozolomide expression) MGMT (low Neuroendocrine tumors Temozolomide expression) MGMT (high default Temozolomide expression) MGMT (low default Temozolomide expression) MRP1 (high Breast Cyclophosphamide expression) MRP1 (low Breast Cyclophosphamide expression) MRP1 (high Small Cell Lung Cancer Etoposide expression) (SCLC) MRP1 (low Small Cell Lung Cancer Etoposide expression) (SCLC) MRP1 (high Nodal Diffuse Large B- Cyclophosphamide/Vincristine expression) Cell Lymphoma MRP1 (low Nodal Diffuse Large B- Cyclophosphamide/Vincristine expression) Cell Lymphoma MRP1 (high default Cyclophosphamide, expression) Etoposide, Vincristine MRP1 (low default Cyclophosphamide, expression) Etoposide, Vincristine PDGFRA (high Malignant Solitary Fibrous Imatinib expression) Tumor of the Pleura (MSFT) PDGFRA (high Gastrointestinal stromal Imatinib expression) tumor (GIST) PDGFRA (high Default Imatinib expression) p-glycoprotein (high Acute myeloid leukemia Etoposide expression) (AML) p-glycoprotein (low Acute myeloid leukemia Etoposide expression) (AML) p-glycoprotein (high Diffuse Large B-cell Doxorubicin expression) Lymphoma (DLBCL) p-glycoprotein (low Diffuse Large B-cell Doxorubicin expression) Lymphoma (DLBCL) p-glycoprotein (high Lung Etoposide expression) p-glycoprotein (low Lung Etoposide expression) p-glycoprotein (high Breast Doxorubicin expression) p-glycoprotein (low Breast Doxorubicin expression) p-glycoprotein (high Ovarian Paclitaxel expression) p-glycoprotein (low Ovarian Paclitaxel expression) p-glycoprotein (high Head and neck squamous Vincristine expression) cell carcinoma (HNSCC) p-glycoprotein (low Head and neck squamous Vincristine expression) cell carcinoma (HNSCC) p-glycoprotein (high default Vincristine, Etoposide, expression) Doxorubicin, Paclitaxel p-glycoprotein (low default Vincristine, Etoposide, expression) Doxorubicin, Paclitaxel PR (high expression) Breast Chemoendocrine therapy Tamoxifen, Anastrazole, Letrozole PR (low expression) default Chemoendocrine therapy Tamoxifen, Anastrazole, Letrozole PTEN (high Breast Trastuzumab expression) PTEN (low Breast Trastuzumab expression) PTEN (high Non-small cell Lung Gefitinib expression) Cancer (NSCLC) PTEN (low Non-small cell Lung Gefitinib expression) Cancer (NSCLC) PTEN (high Colorectal Cetuximab, Panitumumab expression) PTEN (low Colorectal Cetuximab, Panitumumab expression) PTEN (high Glioblastoma Erlotinib, Gefitinib expression) PTEN (low Glioblastoma Erlotinib, Gefitinib expression) PTEN (high default Cetuximab, Panitumumab, expression) Erlotinib, Gefitinib and Trastuzumab PTEN (low default Cetuximab, Panitumumab, expression) Erlotinib, Gefitinib and Trastuzumab RRM1 (high Non-small cell lung cancer Gemcitabine experssion) (NSCLC) RRM1 (low Non-small cell lung cancer Gemcitabine expression) (NSCLC) RRM1 (high Pancreas Gemcitabine experssion) RRM1 (low Pancreas Gemcitabine expression) RRM1 (high default Gemcitabine experssion) RRM1 (low default Gemcitabine expression) SPARC (high Breast nab-paclitaxel expression) SPARC (high default nab-paclitaxel expression) TS (high expression) Colorectal fluoropyrimidines TS (low expression) Colorectal fluoropyrimidines TS (high expression) Pancreas fluoropyrimidines TS (low expression) Pancreas fluoropyrimidines TS (high expression) Head and Neck Cancer fluoropyrimidines TS (low expression) Head and Neck Cancer fluoropyrimidines TS (high expression) Gastric fluoropyrimidines TS (low expression) Gastric fluoropyrimidines TS (high expression) Non-small cell lung cancer fluoropyrimidines (NSCLC) TS (low expression) Non-small cell lung cancer fluoropyrimidines (NSCLC) TS (high expression) Liver fluoropyrimidines TS (low expression) Liver fluoropyrimidines TS (high expression) default fluoropyrimidines TS (low expression) default fluoropyrimidines TOPO1 (high Colorectal Irinotecan expression) TOPO1 (low Colorectal Irinotecan expression) TOPO1 (high Ovarian Irinotecan expression) TOPO1 (low Ovarian Irinotecan expression) TOPO1 (high default Irinotecan expression) TOPO1 (low default Irinotecan expression) TopoIIa (high Breast Doxorubicin, liposomal- epxression) Doxorubicin, Epirubicin TopoIIa (low Breast Doxorubicin, liposomal- expression) Doxorubicin, Epirubicin TopoIIa (high default Doxorubicin, liposomal- epxression) Doxorubicin, Epirubicin TopoIIa (low default Doxorubicin, liposomal- expression) Doxorubicin, Epirubicin

Other examples of biomarkers that can be detected and the treatment agents that can be selected or possibly avoided based on the biomarker signatures are listed in Table 13. For example, for a subject suffering from cancer, detecting overexpression of ADA in vesicles from a subject is used to classify the subject as a responder to pentostatin, or pentostatin identified as an agent to use for treating the subject. In another example, for a subject suffering from cancer, detecting overexpression of BCRP in vesicles from the subject is used to classify the subject as a non-responder to cisplatin, carboplatin, irinotecan, and topotecan, meaning that cisplatin, carboplatin, irinotecan, and topotecan are identified as agents that are suboptimal for treating the subject.

TABLE 13 Examples of Biomarkers, Agents and Resistance Gene Name Expression Status Candidate Agent(s) Possible Resistance ADA Overexpressed pentostatin ADA Underexpressed cytarabine AR Overexpressed abarelix, bicalutamide, flutamide, gonadorelin, goserelin, leuprolide ASNS Underexpressed asparaginase, pegaspargase BCRP (ABCG2) Overexpressed cisplatin, carboplatin, irinotecan, topotecan BRCA1 Underexpressed mitomycin BRCA2 Underexpressed mitomycin CD52 Overexpressed alemtuzumab CDA Overexpressed cytarabine c-erbB2 High levels of Trastuzumab, c-erbB2 phosphorylation in kinase inhibitor, lapatinib epithelial cells CES2 Overexpressed irinotecan c-kit Overexpressed sorafenib, sunitinib, imatinib COX-2 Overexpressed celecoxib DCK Overexpressed gemcitabine cytarabine DHFR Underexpressed methotrexate, pemetrexed DHFR Overexpressed methotrexate DNMT1 Overexpressed azacitidine, decitabine DNMT3A Overexpressed azacitidine, decitabine DNMT3B Overexpressed azacitidine, decitabine EGFR Overexpressed erlotinib, gefitinib, cetuximab, panitumumab EML4-ALK Overexpressed (present) crizotinib EPHA2 Overexpressed dasatinib ER Overexpressed anastrazole, exemestane, fulvestrant, letrozole, megestrol, tamoxifen, medroxyprogesterone, toremifene, aminoglutethimide ERCC1 Overexpressed carboplatin, cisplatin GART Underexpressed pemetrexed GRN (PCDGF, PGRN) Overexpressed anti-oestrogen therapy, tamoxifen, faslodex, letrozole, herceptin in Her-2 overexpressing cells, doxorubicin HER-2 (ERBB2) Overexpressed trastuzumab, lapatinib HIF-1α Overexpressed sorafenib, sunitinib, bevacizumab IκB-α Overexpressed bortezomib MGMT Underexpressed temozolomide MGMT Overexpressed temozolomide MRP1 (ABCC1) Overexpressed etoposide, paclitaxel, docetaxel, vinblastine, vinorelbine, topotecan, teniposide P-gp (ABCB1) Overexpressed doxorubicin, etoposide, epirubicin, paclitaxel, docetaxel, vinblastine, vinorelbine, topotecan, teniposide, liposomal doxorubicin PDGFR-α Overexpressed sorafenib, sunitinib, imatinib PDGFR-β Overexpressed sorafenib, sunitinib, imatinib PR Overexpressed exemestane, fulvestrant, gonadorelin, goserelin, medroxyprogesterone, megestrol, tamoxifen, toremifene RARA Overexpressed ATRA RRM1 Underexpressed gemcitabine, hydroxyurea RRM2 Underexpressed gemcitabine, hydroxyurea RRM2B Underexpressed gemcitabine, hydroxyurea RXR-α Overexpressed bexarotene RXR-β Overexpressed bexarotene SPARC Overexpressed nab-paclitaxel SRC Overexpressed dasatinib SSTR2 Overexpressed octreotide SSTR5 Overexpressed octreotide TOPO I Overexpressed irinotecan, topotecan TOPO IIα Overexpressed doxorubicin, epirubicin, liposomal-doxorubicin TOPO IIβ Overexpressed doxorubicin, epirubicin, liposomal-doxorubicin TS Underexpressed capecitabine, 5- fluorouracil, pemetrexed TS Overexpressed capecitabine, 5- fluorouracil VDR Overexpressed calcitriol, cholecalciferol VEGFR1 (Flt1) Overexpressed sorafenib, sunitinib, bevacizumab VEGFR2 Overexpressed sorafenib, sunitinib, bevacizumab VHL Underexpressed sorafenib, sunitinib

Further drug associations and rules that are used in embodiments of the invention are found in U.S. patent application Ser. No. 12/658,770, filed Feb. 12, 2010; International PCT Patent Application PCT/US2010/000407, filed Feb. 11, 2010; International PCT Patent Application PCT/US2010/54366, filed Oct. 27, 2010; and U.S. Provisional Patent Application 61/427,788, filed Dec. 28, 2010; all of which applications are incorporated by reference herein in their entirety. See, e.g., “Table 4: Rules Summary for Treatment Selection” of PCT/US2010/54366.

Any treatment-associated target can be part of a biosignature for providing a theranosis. A “druggable target” comprising a target that can be modulated with a therapeutic agent such as a small molecule or biologic, is a candidate for inclusion in the biosignature of the invention. Treatment-associated targets also include biomarkers that can confer resistance to a treatment, such as shown in Tables 12 and 13. The biosignature can be based on either the gene, e.g., DNA sequence, and/or gene product, e.g., mRNA or protein, or the treatment-associated target. Such nucleic acid and/or polypeptide can be profiled as applicable as to presence or absence, level or amount, activity, mutation, sequence, haplotype, rearrangement, copy number, or other measurable characteristic. The gene or gene product can be associated with a vesicle population, e.g., as a vesicle surface marker or as vesicle payload. In an embodiment, the invention provides a method of theranosing a cancer, comprising identifying a biosignature that comprises a presence or level of one or more treatment-associated target, and selecting a candidate therapeutic based on the biosignature. The treatment-associated target can be a circulating biomarker, a vesicle, or a vesicle associated biomarker. Because treatment-associated targets can be independent of the tissue or cell-of-origin, biosignatures comprising treatment-associated targets can be used to provide a theranosis for any proliferative disease, such as cancers from various anatomical origins, including cancers of unknown origin such as CUPS.

The treatment-associated or treatment associated targets assessed using the methods of the invention comprise without limitation ABCC1, ABCG2, ACE2, ADA, ADH1C, ADH4, AGT, AR, AREG, ASNS, BCL2, BCRP, BDCA1, beta III tubulin, BIRC5, B-RAF, BRCA1, BRCA2, CA2, caveolin, CD20, CD25, CD33, CD52, CDA, CDKN2A, CDKN1A, CDKN1B, CDK2, CDW52, CES2, CK 14, CK 17, CK 5/6, c-KIT, c-Met, c-Myc, COX-2, Cyclin D1, DCK, DHFR, DNMT1, DNMT3A, DNMT3B, E-Cadherin, ECGF1, EGFR, EML4-ALK fusion, EPHA2, Epiregulin, ER, ERBR2, ERCC1, ERCC3, EREG, ESR1, FLT1, folate receptor, FOLR1, FOLR2, FSHB, FSHPRH1, FSHR, FYN, GART, GNRH1, GNRHR1, GSTP1, HCK, HDAC1, hENT-1, Her2/Neu, HGF, HIF1A, HIG1, HSP90, HSP90AA1, HSPCA, IGF-1R, IGFRBP, IGFRBP3, IGFRBP4, IGFRBP5, IL13RA1, IL2RA, KDR, Ki67, KIT, K-RAS, LCK, LTB, Lymphotoxin Beta Receptor, LYN, MET, MGMT, MLH1, MMR, MRP1, MS4A1, MSH2, MSH5, Myc, NFKB1, NFKB2, NFKBIA, ODC1, OGFR, p16, p21, p27, p53, p95, PARP-1, PDGFC, PDGFR, PDGFRA, PDGFRB, PGP, PGR, PI3K, POLA, POLA1, PPARG, PPARGC1, PR, PTEN, PTGS2, RAF1, RARA, RRM1, RRM2, RRM2B, RXRB, RXRG, SPARC, SRC, SSTR1, SSTR2, SSTR3, SSTR4, SSTR5, Survivin, TK1, TLE3, TNF, TOP1, TOP2A, TOP2B, TS, TXN, TXNRD1, TYMS, VDR, VEGF, VEGFA, VEGFC, VHL, YES1, ZAP70, or any combination thereof. A biosignature including one or combination of these markers can be used to characterize a phenotype according to the invention, such as providing a theranosis. These markers are known to play a role in the efficacy of various chemotherapeutic agents against proliferative diseases. Accordingly, the markers can be assessed to select a candidate treatment for the cancer independent of the origin or type of cancer. In an embodiment, the invention provides a method of selecting a candidate therapeutic for a cancer, comprising identifying a biosignature comprising a level or presence of one or more treatment associated target, and selecting the candidate therapeutic based on its predicted efficacy for a patient with the biosignature. The one or more treatment-associated target can be one of the targets listed herein, e.g., in Tables 12-14. In some embodiments, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 35, 40, 45, or at least 50 of the one or more treatment-associated targets are assessed. The one or more treatment-associated target can be associated with a vesicle, e.g., as a vesicle surface marker or as vesicle payload as either nucleic acid (e.g., DNA, mRNA) or protein. In some embodiments, the presence or level of a microRNA known to interact with the one or more treatment-associated target is assessed, wherein a high level of microRNA known to suppress the one or more treatment-associated target can indicate a lower expression of the one or more treatment-associated target and thus a lower likelihood of response to a treatment against the treatment-associated target. The one or more treatment-associated target can be circulating biomarkers. The one or more treatment-associated target can be assessed in a tissue sample. The predicted efficacy can be determined by comparing the presence or level of the one or more treatment-associated target to a reference value, wherein a higher level that the reference indicates that the subject is a likely responder. The predicted efficacy can be determined using a classifier algorithm, wherein the classifier was trained by comparing the biosignature of the one or more treatment-associated target in subjects that are known to be responders or non-responders to the candidate treatment. Molecular associations of the one or more treatment-associated target with appropriate candidate targets are displayed in Tables 11-13 herein and U.S. patent application Ser. No. 12/658,770, filed Feb. 12, 2010; International PCT Patent Application PCT/US2010/000407, filed Feb. 11, 2010; International PCT Patent Application PCT/US2010/54366, filed Oct. 27, 2010; and U.S. Provisional Patent Application 61/427,788, filed Dec. 28, 2010; all of which applications are incorporated by reference herein in their entirety.

Table 14 provides a listing of gene and corresponding protein symbols and names of many of the theranostic targets that are analyzed according to the methods of the invention. As understood by those of skill in the art, genes and proteins have developed a number of alternative names in the scientific literature. Thus, the listing in Table 14 comprises an illustrative but not exhaustive compilation. A further listing of gene aliases and descriptions can be found using a variety of online databases, including GeneCards® (www.genecards.org), HUGO Gene Nomenclature (www.genenames.org), Entrez Gene (www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene), UniProtKB/Swiss-Prot (www.uniprot.org), UniProtKB/TrEMBL (www.uniprot.org), OMIM (www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM), GeneLoc (genecards.weizmann.ac.il/geneloc/), and Ensembl (www.ensembl.org). Generally, gene symbols and names below correspond to those approved by HUGO, and protein names are those recommended by UniProtKB/Swiss-Prot. Common alternatives are provided as well. Where a protein name indicates a precursor, the mature protein is also implied. Throughout the application, gene and protein symbols may be used interchangeably and the meaning can be derived from context as necessary.

TABLE 14 Genes and Related Proteins for Cancer Theranostics Gene Symbol Gene Name Protein Symbol Protein Name ABCB1, ATP-binding cassette, sub-family B ABCB1, Multidrug resistance protein 1; PGP (MDR/TAP), member 1 MDR1, PGP P-glycoprotein ABCC1, ATP-binding cassette, sub-family C MRP1, Multidrug resistance-associated protein 1 MRP1 (CFTR/MRP), member 1 ABCC1 ABCG2, ATP-binding cassette, sub-family G ABCG2 ATP-binding cassette sub-family G BCRP (WHITE), member 2 member 2 ACE2 angiotensin I converting enzyme ACE2 Angiotensin-converting enzyme 2 (peptidyl-dipeptidase A) 2 precursor ADA adenosine deaminase ADA Adenosine deaminase ADH1C alcohol dehydrogenase 1C (class I), ADH1G Alcohol dehydrogenase 1C gamma polypeptide ADH4 alcohol dehydrogenase 4 (class II), pi ADH4 Alcohol dehydrogenase 4 polypeptide AGT angiotensinogen (serpin peptidase ANGT, Angiotensinogen precursor inhibitor, clade A, member 8) AGT ALK anaplastic lymphoma receptor tyrosine ALK ALK tyrosine kinase receptor precursor kinase AR androgen receptor AR Androgen receptor AREG amphiregulin AREG Amphiregulin precursor ASNS asparagine synthetase ASNS Asparagine synthetase [glutamine- hydrolyzing] BCL2 B-cell CLL/lymphoma 2 BCL2 Apoptosis regulator Bcl-2 BDCA1, CD1c molecule CD1C T-cell surface glycoprotein CD1c precursor CD1C BIRC5 baculoviral IAP repeat-containing 5 BIRC5, Baculoviral IAP repeat-containing protein Survivin 5; Survivin BRAF v-raf murine sarcoma viral oncogene B-RAF, Serine/threonine-protein kinase B-raf homolog B1 BRAF BRCA1 breast cancer 1, early onset BRCA1 Breast cancer type 1 susceptibility protein BRCA2 breast cancer 2, early onset BRCA2 Breast cancer type 2 susceptibility protein CA2 carbonic anhydrase II CA2 Carbonic anhydrase 2 CAV1 caveolin 1, caveolae protein, 22 kDa CAV1 Caveolin-1 CCND1 cyclin D1 CCND1, G1/S-specific cyclin-D1 Cyclin D1, BCL-1 CD20, membrane-spanning 4-domains, CD20 B-lymphocyte antigen CD20 MS4A1 subfamily A, member 1 CD25, interleukin 2 receptor, alpha CD25 Interleukin-2 receptor subunit alpha IL2RA precursor CD33 CD33 molecule CD33 Myeloid cell surface antigen CD33 precursor CD52, CD52 molecule CD52 CAMPATH-1 antigen precursor CDW52 CDA cytidine deaminase CDA Cytidine deaminase CDH1, cadherin 1, type 1, E-cadherin E-Cad Cadherin-1 precursor (E-cadherin) ECAD (epithelial) CDK2 cyclin-dependent kinase 2 CDK2 Cell division protein kinase 2 CDKN1A, cyclin-dependent kinase inhibitor 1A CDKN1A, p21 Cyclin-dependent kinase inhibitor 1 P21 (p21, Cip1) CDKN1B cyclin-dependent kinase inhibitor 1B CDKN1B, p27 Cyclin-dependent kinase inhibitor 1B (p27, Kip1) CDKN2A, cyclin-dependent kinase inhibitor 2A CD21A, p16 Cyclin-dependent kinase inhibitor 2A, P16 (melanoma, p16, inhibits CDK4) isoforms 1/2/3 CES2 carboxylesterase 2 (intestine, liver) CES2, EST2 Carboxylesterase 2 precursor CK 5/6 cytokeratin 5/cytokeratin 6 CK 5/6 Keratin, type II cytoskeletal 5; Keratin, type II cytoskeletal 6 CK14, keratin 14 CK14 Keratin, type I cytoskeletal 14 KRT14 CK17, keratin 17 CK17 Keratin, type I cytoskeletal 17 KRT17 COX2, prostaglandin-endoperoxide synthase 2 COX-2, Prostaglandin G/H synthase 2 precursor PTGS2 (prostaglandin G/H synthase and PTGS2 cyclooxygenase) DCK deoxycytidine kinase DCK Deoxycytidine kinase DHFR dihydrofolate reductase DHFR Dihydrofolate reductase DNMT1 DNA (cytosine-5-)-methyltransferase 1 DNMT1 DNA (cytosine-5)-methyltransferase 1 DNMT3A DNA (cytosine-5-)-methyltransferase 3 DNMT3A DNA (cytosine-5)-methyltransferase 3A alpha DNMT3B DNA (cytosine-5-)-methyltransferase 3 DNMT3B DNA (cytosine-5)-methyltransferase 3B beta ECGF1, thymidine phosphorylase TYMP, PD- Thymidine phosphorylase precursor TYMP ECGF, ECDF1 EGFR, epidermal growth factor receptor EGFR, Epidermal growth factor receptor ERBB1, (erythroblastic leukemia viral (v-erb-b) ERBB1, precursor HER1 oncogene homolog, avian) HER1 EML4 echinoderm microtubule associated EML4 Echinoderm microtubule-associated protein like 4 protein-like 4 EPHA2 EPH receptor A2 EPHA2 Ephrin type-A receptor 2 precursor ER, ESR1 estrogen receptor 1 ER, ESR1 Estrogen receptor ERBB2, v-erb-b2 erythroblastic leukemia viral ERBB2, Receptor tyrosine-protein kinase erbB-2 HER2/NEU oncogene homolog 2, HER2, precursor neuro/glioblastoma derived oncogene HER-2/neu homolog (avian) ERCC1 excision repair cross-complementing ERCC1 DNA excision repair protein ERCC-1 rodent repair deficiency, complementation group 1 (includes overlapping antisense sequence) ERCC3 excision repair cross-complementing ERCC3 TFIIH basal transcription factor complex rodent repair deficiency, helicase XPB subunit complementation group 3 (xeroderma pigmentosum group B complementing) EREG Epiregulin EREG Proepiregulin precursor FLT1 fms-related tyrosine kinase 1 (vascular FLT-1, Vascular endothelial growth factor receptor endothelial growth factor/vascular VEGFR1 1 precursor permeability factor receptor) FOLR1 folate receptor 1 (adult) FOLR1 Folate receptor alpha precursor FOLR2 folate receptor 2 (fetal) FOLR2 Folate receptor beta precursor FSHB follicle stimulating hormone, beta FSHB Follitropin subunit beta precursor polypeptide FSHPRH1, centromere protein I FSHPRH1, Centromere protein I CENP1 CENP1 FSHR follicle stimulating hormone receptor FSHR Follicle-stimulating hormone receptor precursor FYN FYN oncogene related to SRC, FGR, FYN Tyrosine-protein kinase Fyn YES GART phosphoribosylglycinamide GART, Trifunctional purine biosynthetic protein formyltransferase, PUR2 adenosine-3 phosphoribosylglycinamide synthetase, phosphoribosylaminoimidazole synthetase GNRH1 gonadotropin-releasing hormone 1 GNRH1, Progonadoliberin-1 precursor (luteinizing-releasing hormone) GON1 GNRHR1, gonadotropin-releasing hormone GNRHR1 Gonadotropin-releasing hormone receptor GNRHR receptor GSTP1 glutathione S-transferase pi 1 GSTP1 Glutathione S-transferase P HCK hemopoietic cell kinase HCK Tyrosine-protein kinase HCK HDAC1 histone deacetylase 1 HDAC1 Histone deacetylase 1 HGF hepatocyte growth factor (hepapoietin HGF Hepatocyte growth factor precursor A; scatter factor) HIF1A hypoxia inducible factor 1, alpha HIF1A Hypoxia-inducible factor 1-alpha subunit (basic helix-loop-helix transcription factor) HIG1, HIG1 hypoxia inducible domain HIG1, HIG1 domain family member 1A HIGD1A, family, member 1A HIGD1A, HIG1A HIG1A HSP90AA1, heat shock protein 90 kDa alpha HSP90, Heat shock protein HSP 90-alpha HSP90, (cytosolic), class A member 1 HSP90A HSPCA IGF1R insulin-like growth factor 1 receptor IGF-1R Insulin-like growth factor 1 receptor precursor IGFBP3, insulin-like growth factor binding IGFBP-3, Insulin-like growth factor-binding protein IGFRBP3 protein 3 IBP-3 3 precursor IGFBP4, insulin-like growth factor binding IGFBP-4, Insulin-like growth factor-binding protein IGFRBP4 protein 4 IBP-4 4 precursor IGFBP5, insulin-like growth factor binding IGFBP-5, Insulin-like growth factor-binding protein IGFRBP5 protein 5 IBP-5 5 precursor IL13RA1 interleukin 13 receptor, alpha 1 IL-13RA1 Interleukin-13 receptor subunit alpha-1 precursor KDR kinase insert domain receptor (a type KDR, Vascular endothelial growth factor receptor III receptor tyrosine kinase) VEGFR2 2 precursor KIT, c-KIT v-kit Hardy-Zuckerman 4 feline KIT, c-KIT Mast/stem cell growth factor receptor sarcoma viral oncogene homolog CD117, precursor SCFR KRAS v-Ki-ras2 Kirsten rat sarcoma viral K-RAS GTPase KRas precursor oncogene homolog LCK lymphocyte-specific protein tyrosine LCK Tyrosine-protein kinase Lck kinase LTB lymphotoxin beta (TNF superfamily, LTB, TNF3 Lymphotoxin-beta member 3) LTBR lymphotoxin beta receptor (TNFR LTBR, Tumor necrosis factor receptor superfamily superfamily, member 3) LTBR3, member 3 precursor TNFR LYN v-yes-1 Yamaguchi sarcoma viral LYN Tyrosine-protein kinase Lyn related oncogene homolog MET, c- met proto-oncogene (hepatocyte MET, c- Hepatocyte growth factor receptor MET growth factor receptor) MET precursor MGMT O-6-methylguanine-DNA MGMT Methylated-DNA--protein-cysteine methyltransferase methyltransferase MKI67, antigen identified by monoclonal Ki67, Ki-67 Antigen KI-67 KI67 antibody Ki-67 MLH1 mutL homolog 1, colon cancer, MLH1 DNA mismatch repair protein Mlh1 nonpolyposis type 2 (E. coli) MMR mismatch repair (refers to MLH1, MSH2, MSH5) MSH2 mutS homolog 2, colon cancer, MSH2 DNA mismatch repair protein Msh2 nonpolyposis type 1 (E. coli) MSH5 mutS homolog 5 (E. coli) MSH5, MutS protein homolog 5 hMSH5 MYC, c- v-myc myelocytomatosis viral MYC, c- Myc proto-oncogene protein MYC oncogene homolog (avian) MYC NBN, P95 nibrin NBN, p95 Nibrin NDGR1 N-myc downstream regulated 1 NDGR1 Protein NDGR1 NFKB1 nuclear factor of kappa light NFKB1 Nuclear factor NF-kappa-B p105 subunit polypeptide gene enhancer in B-cells 1 NFKB2 nuclear factor of kappa light NFKB2 Nuclear factor NF-kappa-B p100 subunit polypeptide gene enhancer in B-cells 2 (p49/p100) NFKBIA nuclear factor of kappa light NFKBIA NF-kappa-B inhibitor alpha polypeptide gene enhancer in B-cells inhibitor, alpha ODC1 ornithine decarboxylase 1 ODC Ornithine decarboxylase OGFR opioid growth factor receptor OGFR Opioid growth factor receptor PARP1 poly (ADP-ribose) polymerase 1 PARP-1 Poly [ADP-ribose] polymerase 1 PDGFC platelet derived growth factor C PDGF-C, Platelet-derived growth factor C precursor VEGF-E PDGFR platelet-derived growth factor receptor PDGFR Platelet-derived growth factor receptor PDGFRA platelet-derived growth factor receptor, PDGFRA, Alpha-type platelet-derived growth factor alpha polypeptide PDGFR2, receptor precursor CD140 A PDGFRB platelet-derived growth factor receptor, PDGFRB, Beta-type platelet-derived growth factor beta polypeptide PDGFR, receptor precursor PDGFR1, CD140 B PGR progesterone receptor PR Progesterone receptor PIK3CA phosphoinositide-3 -kinase, catalytic, PI3K subunit phosphoinositide-3-kinase, catalytic, alpha alpha polypeptide p110α polypeptide POLA1 polymerase (DNA directed), alpha 1, POLA, DNA polymerase alpha catalytic subunit catalytic subunit; polymerase (DNA POLA1 directed), alpha, polymerase (DNA p180 directed), alpha 1 PPARG, peroxisome proliferator-activated PPARG Peroxisome proliferator-activated receptor PPARG1, receptor gamma gamma PPARG2, PPAR- gamma, NR1C3 PPARGC1A, peroxisome proliferator-activated PGC-1- Peroxisome proliferator-activated receptor LEM6, receptor gamma, coavtivator 1 alpha alpha, gamma coactivator 1-alpha; PPAR-gamma PGC1, PPARGC-1- coactivator 1-alpha PGC1A, alpha PPARGC1 PSMD9, P27 proteasome (prosome, macropain) 26S p27 26S proteasome non-ATPase regulatory subunit, non-ATPase, 9 subunit 9 PTEN phosphatase and tensin homolog PTEN Phosphatidylinositol-3,4,5-triphosphate 3- MMAC1, phosphatase and dual-specificity protein TEP1 phosphatase; Mutated in multiple advanced cancers 1 PTPN12 protein tyrosine phosphatase, non- PTPG1 Tyrosine-protein phosphatase non-receptor receptor type 12 type 12; Protein-tyrosine phosphatase G1 RAF1 v-raf-1 murine leukemia viral oncogene RAF, RAF-1, RAF proto-oncogene serine/threonine-protein homolog 1 c-RAF kinase RARA retinoic acid receptor, alpha RAR, RAR- Retinoic acid receptor alpha alpha, RARA RRM1 ribonucleotide reductase M1 RRM1, RR1 Ribonucleoside-diphosphate reductase large subunit RRM2 ribonucleotide reductase M2 RRM2, Ribonucleoside-diphosphate reductase RR2M, RR2 subunit M2 RRM2B ribonucleotide reductase M2 B (TP53 RRM2B, Ribonucleoside-diphosphate reductase inducible) P53R2 subunit M2 B RXRB retinoid X receptor, beta RXRB Retinoic acid receptor RXR-beta RXRG retinoid X receptor, gamma RXRG, Retinoic acid receptor RXR-gamma RXRC SIK2 salt-inducible kinase 2 SIK2, Salt-inducible protein kinase 2; Q9H0K1 Serine/threonine-protein kinase SIK2 SLC29A1 solute carrier family 29 (nucleoside ENT-1 Equilibrative nucleoside transporter 1 transporters), member 1 SPARC secreted protein, acidic, cysteine-rich SPARC SPARC precursor; Osteonectin (osteonectin) SRC v-src sarcoma (Schmidt-Ruppin A-2) SRC Proto-oncogene tyrosine-protein kinase viral oncogene homolog (avian) Src SSTR1 somatostatin receptor 1 SSTR1, Somatostatin receptor type 1 SSR1, SS1R SSTR2 somatostatin receptor 2 SSTR2, Somatostatin receptor type 2 SSR2, SS2R SSTR3 somatostatin receptor 3 SSTR3, Somatostatin receptor type 3 SSR3, SS3R SSTR4 somatostatin receptor 4 SSTR4, Somatostatin receptor type 4 SSR4, SS4R SSTR5 somatostatin receptor 5 SSTR5, Somatostatin receptor type 5 SSR5, SS5R TK1 thymidine kinase 1, soluble TK1, KITH Thymidine kinase, cytosolic TLE3 transducin-like enhancer of split 3 TLE3 Transducin-like enhancer protein 3 (E(sp1) homolog, Drosophila) TNF tumor necrosis factor (TNF TNF, TNF- Tumor necrosis factor precursor superfamily, member 2) alpha, TNF-a TOP1, topoisomerase (DNA) I TOP1, DNA topoisomerase 1 TOPO1 TOPO1 TOP2A, topoisomerase (DNA) II alpha 170 kDa TOP2A, DNA topoisomerase 2-alpha; TOPO2A TOP2, Topoisomerase II alpha TOPO2A TOP2B, topoisomerase (DNA) II beta 180 kDa TOP2B, DNA topoisomerase 2-beta; TOPO2B TOPO2B Topoisomerase II beta TP53 tumor protein p53 p53 Cellular tumor antigen p53 TUBB3 tubulin, beta 3 Beta III Tubulin beta-3 chain tubulin, TUBB3, TUBB4 TXN thioredoxin TXN, TRX, Thioredoxin TRX-1 TXNRD1 thioredoxin reductase 1 TXNRD1, Thioredoxin reductase 1, cytoplasmic; TXNR Oxidoreductase TYMS, TS thymidylate synthetase TYMS, TS Thymidylate synthase VDR vitamin D (1,25-dihydroxyvitamin D3) VDR Vitamin D3 receptor receptor VEGFA, vascular endothelial growth factor A VEGF-A, Vascular endothelial growth factor A VEGF VEGF precursor VEGFC vascular endothelial growth factor C VEGF-C Vascular endothelial growth factor C precursor VHL von Hippel-Lindau tumor suppressor VHL Von Hippel-Lindau disease tumor suppressor YES1 v-yes-1 Yamaguchi sarcoma viral YES1, Yes, Proto-oncogene tyrosine-protein kinase oncogene homolog 1 p61-Yes Yes ZAP70 zeta-chain (TCR) associated protein ZAP-70 Tyrosine-protein kinase ZAP-70 kinase 70 kDa

Genes and gene products that are known to play a role in cancer and can be included in a biosignature of the invention include without limitation 2AR, A DISINTEGRIN, ACTIVATOR OF THYROID AND RETINOIC ACID RECEPTOR (ACTR), ADAM 11, ADIPOGENESIS INHIBITORY FACTOR (ADIF), ALPHA 6 INTEGRIN SUBUNIT, ALPHA V INTEGRIN SUBUNIT, ALPHA-CATENIN, AMPLIFIED IN BREAST CANCER 1 (AIB1), AMPLIFIED IN BREAST CANCER 3 (AIB3), AMPLIFIED IN BREAST CANCER 4 (AIB4), AMYLOID PRECURSOR PROTEIN SECRETASE (APPS), AP-2 GAMMA, APPS, ATP-BINDING CASSETTE TRANSPORTER (ABCT), PLACENTA-SPECIFIC (ABCP), ATP-BINDING CASSETTE SUBFAMILY C MEMBER (ABCC1), BAG-1, BASIGIN (BSG), BCEI, B-CELL DIFFERENTIATION FACTOR (BCDF), B-CELL LEUKEMIA 2 (BCL-2), B-CELL STIMULATORY FACTOR-2 (BSF-2), BCL-1, BCL-2-ASSOCIATED X PROTEIN (BAX), BCRP, BETA 1 INTEGRIN SUBUNIT, BETA 3 INTEGRIN SUBUNIT, BETA 5 INTEGRIN SUBUNIT, BETA-2 INTERFERON, BETA-CATENIN, BETA-CATENIN, BONE SIALOPROTEIN (BSP), BREAST CANCER ESTROGEN-INDUCIBLE SEQUENCE (BCEI), BREAST CANCER RESISTANCE PROTEIN (BCRP), BREAST CANCER TYPE 1 (BRCA1), BREAST CANCER TYPE 2 (BRCA2), BREAST CARCINOMA AMPLIFIED SEQUENCE 2 (BCAS2), CADHERIN, EPITHELIAL CADHERIN-11, CADHERIN-ASSOCIATED PROTEIN, CALCITONIN RECEPTOR (CTR), CALCIUM PLACENTAL PROTEIN (CAPL), CALCYCLIN, CALLA, CAM5, CAPL, CARCINOEMBRYONIC ANTIGEN (CEA), CATENIN, ALPHA 1, CATHEPSIN B, CATHEPSIN D, CATHEPSIN K, CATHEPSIN L2, CATHEPSIN O, CATHEPSIN O1, CATHEPSIN V, CD10, CD146, CD147, CD24, CD29, CD44, CD51, CD54, CD61, CD66e, CD82, CD87, CD9, CEA, CELLULAR RETINOL-BINDING PROTEIN 1 (CRBP1), c-ERBB-2, CK7, CK8, CK18, CK19, CK20, CLAUDIN-7, c-MET, COLLAGENASE, FIBROBLAST, COLLAGENASE, INTERSTITIAL, COLLAGENASE-3, COMMON ACUTE LYMPHOCYTIC LEUKEMIA ANTIGEN (CALLA), CONNEXIN 26 (Cx26), CONNEXIN 43 (Cx43), CORTACTIN, COX-2, CTLA-8, CTR, CTSD, CYCLIN D1, CYCLOOXYGENASE-2, CYTOKERATIN 18, CYTOKERATIN 19, CYTOKERATIN 8, CYTOTOXIC T-LYMPHOCYTE-ASSOCIATED SERINE ESTERASE 8 (CTLA-8), DIFFERENTIATION-INHIBITING ACTIVITY (DIA), DNA AMPLIFIED IN MAMMARY CARCINOMA 1 (DAM1), DNA TOPOISOMERASE II ALPHA, DR-NM23, E-CADHERIN, EMMPRIN, EMS1, ENDOTHELIAL CELL GROWTH FACTOR (ECGR), PLATELET-DERIVED (PD-ECGF), ENKEPHALINASE, EPIDERMAL GROWTH FACTOR RECEPTOR (EGFR), EPISIALIN, EPITHELIAL MEMBRANE ANTIGEN (EMA), ER-ALPHA, ERBB2, ERBB4, ER-BETA, ERF-1, ERYTHROID-POTENTIATING ACTIVITY (EPA), ESR1, ESTROGEN RECEPTOR-ALPHA, ESTROGEN RECEPTOR-BETA, ETS-1, EXTRACELLULAR MATRIX METALLOPROTEINASE INDUCER (EMMPRIN), FIBRONECTIN RECEPTOR, BETA POLYPEPTIDE (FNRB), FIBRONECTIN RECEPTOR BETA SUBUNIT (FNRB), FLK-1, GA15.3, GA733.2, GALECTIN-3, GAMMA-CATENIN, GAP JUNCTION PROTEIN (26 kDa), GAP JUNCTION PROTEIN (43 kDa), GAP JUNCTION PROTEIN ALPHA-1 (GJA1), GAP JUNCTION PROTEIN BETA-2 (GJB2), GCP1, GELATINASE A, GELATINASE B, GELATINASE (72 kDa), GELATINASE (92 kDa), GLIOSTATIN, GLUCOCORTICOID RECEPTOR INTERACTING PROTEIN 1 (GRIP1), GLUTATHIONE S-TRANSFERASE p, GM-CSF, GRANULOCYTE CHEMOTACTIC PROTEIN 1 (GCP1), GRANULOCYTE-MACROPHAGE-COLONY STIMULATING FACTOR, GROWTH FACTOR RECEPTOR BOUND-7 (GRB-7), GSTp, HAP, HEAT-SHOCK COGNATE PROTEIN 70 (HSC70), HEAT-STABLE ANTIGEN, HEPATOCYTE GROWTH FACTOR (HGF), HEPATOCYTE GROWTH FACTOR RECEPTOR (HGFR), HEPATOCYTE-STIMULATING FACTOR III (HSF III), HER-2, HER2/NEU, HERMES ANTIGEN, HET, HHM, HUMORAL HYPERCALCEMIA OF MALIGNANCY (HHM), ICERE-1, INT-1, INTERCELLULAR ADHESION MOLECULE-1 (ICAM-1), INTERFERON-GAMMA-INDUCING FACTOR (IGIF), INTERLEUKIN-1 ALPHA (IL-1A), INTERLEUKIN-1 BETA (IL-1B), INTERLEUKIN-11 (IL-11), INTERLEUKIN-17 (IL-17), INTERLEUKIN-18 (IL-18), INTERLEUKIN-6 (IL-6), INTERLEUKIN-8 (IL-8), INVERSELY CORRELATED WITH ESTROGEN RECEPTOR EXPRESSION-1 (ICERE-1), KAI1, KDR, KERATIN 8, KERATIN 18, KERATIN 19, KISS-1, LEUKEMIA INHIBITORY FACTOR (LIF), LIF, LOST IN INFLAMMATORY BREAST CANCER (LIBC), LOT (“LOST ON TRANSFORMATION”), LYMPHOCYTE HOMING RECEPTOR, MACROPHAGE-COLONY STIMULATING FACTOR, MAGE-3, MAMMAGLOBIN, MASPIN, MC56, M-CSF, MDC, MDNCF, MDR, MELANOMA CELL ADHESION MOLECULE (MCAM), MEMBRANE METALLOENDOPEPTIDASE (MME), MEMBRANE-ASSOCIATED NEUTRAL ENDOPEPTIDASE (NEP), CYSTEINE-RICH PROTEIN (MDC), METASTASIN (MTS-1), MLN64, MMP1, MMP2, MMP3, MMP7, MMP9, MMP11, MMP13, MMP14, MMP15, MMP16, MMP17, MOESIN, MONOCYTE ARGININE-SERPIN, MONOCYTE-DERIVED NEUTROPHIL CHEMOTACTIC FACTOR, MONOCYTE-DERIVED PLASMINOGEN ACTIVATOR INHIBITOR, MTS-1, MUC-1, MUC18, MUCIN LIKE CANCER ASSOCIATED ANTIGEN (MCA), MUCIN, MUC-1, MULTIDRUG RESISTANCE PROTEIN 1 (MDR, MDR1), MULTIDRUG RESISTANCE RELATED PROTEIN-1 (MRP, MRP-1), N-CADHERIN, NEP, NEU, NEUTRAL ENDOPEPTIDASE, NEUTROPHIL-ACTIVATING PEPTIDE 1 (NAP1), NM23-H1, NM23-H2, NME1, NME2, NUCLEAR RECEPTOR COACTIVATOR-1 (NCoA-1), NUCLEAR RECEPTOR COACTIVATOR-2 (NCoA-2), NUCLEAR RECEPTOR COACTIVATOR-3 (NCoA-3), NUCLEOSIDE DIPHOSPHATE KINASE A (NDPKA), NUCLEOSIDE DIPHOSPHATE KINASE B (NDPKB), ONCOSTATIN M (OSM), ORNITHINE DECARBOXYLASE (ODC), OSTEOCLAST DIFFERENTIATION FACTOR (ODF), OSTEOCLAST DIFFERENTIATION FACTOR RECEPTOR (ODFR), OSTEONECTIN (OSN, ON), OSTEOPONTIN (OPN), OXYTOCIN RECEPTOR (OXTR), p27/kip1, p300/CBP COINTEGRATOR ASSOCIATE PROTEIN (p/CIP), p53, p9Ka, PAI-1, PAI-2, PARATHYROID ADENOMATOSIS 1 (PRAD1), PARATHYROID HORMONE-LIKE HORMONE (PTHLH), PARATHYROID HORMONE-RELATED PEPTIDE (PTHrP), P-CADHERIN, PD-ECGF, PDGF, PEANUT-REACTIVE URINARY MUCIN (PUM), P-GLYCOPROTEIN (P-GP), PGP-1, PHGS-2, PHS-2, PIP, PLAKOGLOBIN, PLASMINOGEN ACTIVATOR INHIBITOR (TYPE 1), PLASMINOGEN ACTIVATOR INHIBITOR (TYPE 2), PLASMINOGEN ACTIVATOR (TISSUE-TYPE), PLASMINOGEN ACTIVATOR (UROKINASE-TYPE), PLATELET GLYCOPROTEIN Ma (GP3A), PLAU, PLEOMORPHIC ADENOMA GENE-LIKE 1 (PLAGL1), POLYMORPHIC EPITHELIAL MUCIN (PEM), PRAD1, PROGESTERONE RECEPTOR (PgR), PROGESTERONE RESISTANCE, PROSTAGLANDIN ENDOPEROXIDE SYNTHASE-2, PROSTAGLANDIN G/H SYNTHASE-2, PROSTAGLANDIN H SYNTHASE-2, pS2, PS6K, PSORIASIN, PTHLH, PTHrP, RAD51, RAD52, RAD54, RAP46, RECEPTOR-ASSOCIATED COACTIVATOR 3 (RAC3), REPRESSOR OF ESTROGEN RECEPTOR ACTIVITY (REA), S100A4, S100A6, S100A7, S6K, SART-1, SCAFFOLD ATTACHMENT FACTOR B (SAF-B), SCATTER FACTOR(SF), SECRETED PHOSPHOPROTEIN-1 (SPP-1), SECRETED PROTEIN, ACIDIC AND RICH IN CYSTEINE (SPARC), STANNICALCIN, STEROID RECEPTOR COACTIVATOR-1 (SRC-1), STEROID RECEPTOR COACTIVATOR-2 (SRC-2), STEROID RECEPTOR COACTIVATOR-3 (SRC-3), STEROID RECEPTOR RNA ACTIVATOR (SRA), STROMELYSIN-1, STROMELYSIN-3, TENASCIN-C (TN-C), TESTES-SPECIFIC PROTEASE 50, THROMBOSPONDIN I, THROMBOSPONDIN II, THYMIDINE PHOSPHORYLASE (TP), THYROID HORMONE RECEPTOR ACTIVATOR MOLECULE 1 (TRAM-1), TIGHT JUNCTION PROTEIN 1 (TJP1), TIMP1, TIMP2, TIMP3, TIMP4, TISSUE FACTOR (TF), TISSUE-TYPE PLASMINOGEN ACTIVATOR, TN-C, TP53, tPA, TRANSCRIPTIONAL INTERMEDIARY FACTOR 2 (TIF2), TREFOIL FACTOR 1 (TFF1), TSG101, TSP-1, TSP1, TSP-2, TSP2, TSP50, TUMOR CELL COLLAGENASE STIMULATING FACTOR (TCSF), TUMOR-ASSOCIATED EPITHELIAL MUCIN, uPA, uPAR, UROKINASE, UROKINASE-TYPE PLASMINOGEN ACTIVATOR, UROKINASE-TYPE PLASMINOGEN ACTIVATOR RECEPTOR (uPAR), UVOMORULIN, VASCULAR ENDOTHELIAL GROWTH FACTOR, VASCULAR ENDOTHELIAL GROWTH FACTOR RECEPTOR-2 (VEGFR2), VASCULAR ENDOTHELIAL GROWTH FACTOR-A, VASCULAR PERMEABILITY FACTOR, VEGFR2, VERY LATE T-CELL ANTIGEN BETA (VLA-BETA), VIMENTIN, VITRONECTIN RECEPTOR ALPHA POLYPEPTIDE (VNRA), VITRONECTIN RECEPTOR, VON WILLEBRAND FACTOR, VPF, VWF, WNT-1, ZAC, ZO-1, and ZONULA OCCLUDENS-1. The genes and/or gene products can be part of a biosignature for detecting or theranosing a cancer.

As an illustration, a treatment can be selected for a subject suffering from Non-Small Cell Lung Cancer. One or more biomarkers, such as, but not limited to, EGFR, excision repair cross-complementation group 1 (ERCC1), p53, Ras, p27, class III beta tubulin, breast cancer gene 1 (BRCA1), breast cancer gene 1 (BRCA2), and ribonucleotide reductase messenger 1 (RRM1), can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, Erlotinib, Carboplatin, Paclitaxel, Gefitinib, or a combination thereof.

In another embodiment, a treatment can be selected for a subject suffering from Colorectal Cancer, and a biomarker, such as, but not limited to, K-ras, can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, Panitumumab, Cetuximab, or a combination thereof.

In another embodiment, a treatment can be selected for a subject suffering from Breast Cancer. One or more biomarkers, such as, but not limited to, HER2, toposiomerase II α, estrogen receptor, and progesterone receptor, can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, trastuzumab, anthracyclines, taxane, methotrexate, fluorouracil, or a combination thereof.

In an embodiment, the biomarker comprises EphA2, a receptor tyrosine kinase expressed on the surface of various human tumors, including those associated with breast, lung, prostate and colon cancers. EphA2 is known to be a druggable target. Therefore, the method of assessing whether a subject is likely to respond or not to a therapeutic agent comprises assessing the level of vesicle associated EphA2 in a sample from the subject. The therapeutic agent can comprise agents that bind EphA2, e.g., an antibody or a derivative thereof that targets EphA2. The therapeutic agent can comprise a peptide. The therapeutic agent can comprise an EphA2 vaccine. The therapeutic agents may act as agonist or antagonist of EphA2. EphA2 binding agents whose efficacy can be assessed according to the subject methods include those described in PCT Patent Application Publications WO/2003/094859, entitled “EPHA2 MONOCLONAL ANTIBODIES AND METHODS OF USE THEREOF”; WO/2004/014292, entitled “EphA2 AGONISTIC MONOCLONAL ANTIBODIES AND METHODS OF USE THEREOF”; WO/2004/091375, entitled “EPHA2 AND NON-NEOPLASTIC HYPERPROLIFERATIVE CELL DISORDERS”; WO/2004/091510, entitled “RECOMBINANT IL-9 ANTIBODIES AND USES THEREOF”; WO/2004/092343, entitled “EPHA2, HYPOPROLIFERATIVE CELL DISORDERS AND EPITHELIAL AND ENDOTHELIAL RECONSTITUTION”; WO/2005/012350, entitled “EPHA2 T-CELL EPITOPE AGONISTS AND USES THEREFOR”; WO/2005/016381, entitled “COMBINATION THERAPY FOR THE TREATMENT AND PREVENTION OF CANCER USING EPHA2, PCDGF, AND HAAH”; WO/2005/037233, entitled “LISTERIA-BASED EPHA2 VACCINES”; WO/2005/048917, entitled “USE OF EPHA4 AND MODULATOR OR EPHA4 FOR DIAGNOSIS, TREATMENT AND PREVENTION OF CANCER”; WO/2005/051307, entitled “EPHA2 AGONISTIC MONOCLONAL ANTIBODIES AND METHODS OF USE THEREOF”; WO/2005/055948, entitled “EPHA2, EPHA4 AND LMW-PTP AND METHODS OF TREATMENT OF HYPERPROLIFERATIVE CELL DISORDERS”; WO/2005/056766, entitled “TARGETED DRUG DELIVERY USING EphA20R Eph4 BINDING MOIETIES”; WO/2005/067460, entitled “EPHA2 VACCINES”; WO/2006/023403, entitled “EPH RECEPTOR FC VARIANTS WITH ENHANCED ANTIBODY DEPENDENT CELL-MEDIATED CYTOTOXICITY ACTIVITY”; WO/2006/045110, entitled “HIGH CELL DENSITY PROCESS FOR GROWTH OF LISTERIA”; WO/2006/047637, entitled “USE OF MODULATORS OF EPHA2 AND EPHRINA1 FOR THE TREATMENT AND PREVENTION OF INFECTIONS”; WO/2006/047638, entitled “MODULATORS OF EPHA2 AND EPHRINA1 FOR THE TREATMENT OF FIBROSIS-RELATED DISEASE”; WO/2006/047639, entitled “MODULATION OF ANTIBODY SPECIFICITY BY TAILORING THE AFFINITY TO COGNATE ANTIGENS”; WO/2006/050166, entitled “METHODS OF PREVENTING AND TREATING RSV INFECTIONS AND RELATED CONDITIONS”; WO/2007/030642, entitled “TOXIN CONJUGATED EPH RECEPTOR ANTIBODIES”; WO/2007/073499, entitled “EPHA2 BITE MOLECULES AND USES THEREOF”; WO/2007/075706, entitled “AFFINITY OPTIMIZED EPHA2 AGONISTIC ANTIBODIES AND METHODS OF USE THEREOF”; WO/2007/103261, entitled “LISTERIA-BASED EPHA2 IMMUNOGENIC COMPOSITIONS”; WO/2008/070042, entitled “HIGH POTENCY RECOMBINANT ANTIBODIES, METHODS FOR PRODUCING THEM AND USE IN CANCER THERAPY”; WO/2008/157490, entitled “SYNERGISTIC TREATMENT OF CELLS THAT EXPRESS EPHA2 AND ERBB2; and WO/2009/070642, entitled “PROTEIN FORMULATION”; each of which applications is incorporated herein by reference in its entirety. In some embodiments, the therapeutic agent comprises the anti-EphA2 antibody 1C1 or the anti-EphA2 antibody 1C1-drug conjugate [1C1-maleimidocaproyl-MMAF (mcMMAF)]. See Jackson et al., A Human Antibody—Drug Conjugate Targeting EphA2 Inhibits Tumor Growth In vivo, Cancer Res 2008 68:9367, which is incorporated by reference herein in its entirety. In some embodiments, the therapeutic agent comprises MEDI-547, an anti-EphA2 anti-body drug conjugate directed for use against solid tumors.

Still other druggable targets and associated therapeutic agents whose efficacy can be predicted by assessing the subject's vesicles comprise those described in the follow PCT Patent Publications: WO/2010/041060, entitled “TARGETED BINDING AGENTS DIRECTED TO HEPARANASE AND USES THEREOF”; WO/2010/032061, entitled “ANTIBODIES AGAINST SONIC HEDGEHOG HOMOLOG AND USES THEREOF”; WO/2010/032060, entitled “ANTIBODIES DIRECTED TO DLL4 AND USES THEREOF”; WO/2010/032059, entitled “ANTIBODIES DIRECTED TO CD105 AND USES THEREOF”; WO/2009/097325, entitled “STABILIZED ANGIOPOIETIN-2 ANTIBODIES AND USES THEREOF”; WO/2009/092011, entitled “CYSTEINE ENGINEERED ANTIBODIES FOR SITE-SPECIFIC CONJUGATION”; WO/2009/090268, entitled “PEPTIDE MIMETICS”; WO/2009/058379, entitled “PROTEIN SCAFFOLDS”; WO/2009/018386, entitled “MULTISPECIFIC EPITOPE BINDING PROTEINS AND USES THEREOF”; WO/2008/157490, entitled “SYNERGISTIC TREATMENT OF CELLS THAT EXPRESS EPHA2 AND ERBB2”; WO/2008/137838, entitled “INTERFERON ALPHA-INDUCED PHARMACODYNAMIC MARKERS”; WO/2008/137835, entitled “AUTO-ANTIBODY MARKERS OF AUTOIMMUNE DISEASE”; WO/2008/121615, entitled “ANTIBODY FORMULATION”; WO/2008/114011, entitled “FC POLYPEPTIDE VARIANTS OBTAINED BY RIBOSOME DISPLAY METHODOLOGY”; WO/2008/070137, entitled “INTERFERON ALPHA-INDUCED PHARMACODYNAMIC MARKERS”; WO/2008/065384, entitled “ANTIBODIES SPECIFIC FOR THE COMPLEX OF INTERLEUKIN-6 AND THE INTERLEUKIN-6 RECEPTOR”; WO/2008/065378, entitled “BINDING MEMBERS FOR INTERLEUKIN-6”; WO/2008/042941, entitled “HUMANIZED ANTI-EPHB4 ANTIBODIES AND THEIR USE IN TREATMENT OF ONCOLOGY AND VASCULOGENESIS-RELATED DISEASE”; WO/2007/103261, entitled “LISTERIA-BASED EPHA2 IMMUNOGENIC COMPOSITIONS”; WO/2007/092772, entitled “PROTEIN FORMULATIONS”; WO/2007/084253, entitled “HIGH AFFINITY ANTIBODIES AGAINST HMGB1 AND METHODS OF USE THEREOF”; WO/2007/075706, entitled “AFFINITY OPTIMIZED EPHA2 AGONISTIC ANTIBODIES AND METHODS OF USE THEREOF”; WO/2007/073499, entitled “EPHA2 BITE MOLECULES AND USES THEREOF”; WO/2007/059300, entitled “ANTI-ALK ANTAGONIST AND AGONIST ANTIBODIES AND USES THEREOF”; WO/2007/030642, entitled “TOXIN CONJUGATED EPH RECEPTOR ANTIBODIES”; WO/2006/047639, entitled “MODULATION OF ANTIBODY SPECIFICITY BY TAILORING THE AFFINITY TO COGNATE ANTIGENS”; WO/2006/023420, entitled “INTEGRIN ANTAGONISTS WITH ENHANCED ANTIBODY DEPENDENT CELL-MEDIATED CYTOTOXICITY ACTIVITY”; WO/2006/023403, entitled “EPH RECEPTOR FC VARIANTS WITH ENHANCED ANTIBODY DEPENDENT CELL-MEDIATED CYTOTOXICITY ACTIVITY”; WO/2005/117967, entitled “ANTI-IL-9 ANTIBODY FORMULATIONS AND USES THEREOF”; WO/2005/067460, entitled “EPHA2 VACCINES”; WO/2005/056766, entitled “TARGETED DRUG DELIVERY USING EphA20R Eph4 BINDING MOIETIES”; WO/2005/055948, entitled “EPHA2, EPHA4 AND LMW-PTP AND METHODS OF TREATMENT OF HYPERPROLIFERATIVE CELL DISORDERS”; WO/2005/051307, entitled “EPHA2 AGONISTIC MONOCLONAL ANTIBODIES AND METHODS OF USE THEREOF”; WO/2005/048917, entitled “USE OF EPHA4 AND MODULATOR OR EPHA4 FOR DIAGNOSIS, TREATMENT AND PREVENTION OF CANCER”; WO/2005/037233, entitled “LISTERIA-BASED EPHA2 VACCINES”; WO/2005/016381, entitled “COMBINATION THERAPY FOR THE TREATMENT AND PREVENTION OF CANCER USING EPHA2, PCDGF, AND HAAH”; WO/2005/012350, entitled “EPHA2 T-CELL EPITOPE AGONISTS AND USES THEREFOR”; WO/2005/009363, entitled “TREATMENT OF PRE-CANCEROUS CONDITIONS AND PREVENTION OF CANCER USING PCDGF-BASED THERAPIES”; WO/2005/009217, entitled “DIAGNOSIS OF PRE-CANCEROUS CONDITIONS USING PCDGF AGENTS”; WO/2005/000207, entitled “PCDGF RECEPTOR ANTIBODIES AND METHODS OF USE THEREOF”; WO/2004/066957, entitled “ANTI-INTEGRIN αvβ3 ANTIBODY FORMULATIONS AND USES”; WO/2004/022097, entitled “METHODS OF PREVENTING OR TREATING CELL MALIGNANCIES BY ADMINISTERING CD2 ANTAGONISTS”; WO/2004/014292, entitled “EphA2 AGONISTIC MONOCLONAL ANTIBODIES AND METHODS OF USE THEREOF”; WO/2003/094859, entitled “EPHA2 MONOCLONAL ANTIBODIES AND METHODS OF USE THEREOF”; WO/2003/075957, entitled “THE PREVENTION OR TREATMENT OF CANCER USING INTEGRIN ALPHAVBETA3 ANTAGONISTS IN COMBINATION WITH OTHER AGENTS”; WO/2003/075741, entitled “METHODS OF PREVENTING OR TREATING DISORDERS BY ADMINISTERING AN INTEGRIN αvβ3 ANTAGONIST IN COMBINATION WITH AN HMG-CoA REDUCTASE INHIBITOR OR A BISPHOSPHONATE”; WO/2001/064751, entitled “HIGH POTENCY RECOMBINANT ANTIBODIES AND METHOD FOR PRODUCING THEM”; WO/2001/064248, entitled “METHODS OF ENHANCING ACTIVITY OF VACCINES AND VACCINE COMPOSITIONS”; each of which applications is incorporated herein by reference in its entirety.

In alternate embodiments, the therapeutic may be predicted to be efficacious if a biomarker is not observed in vesicles from the subject, or if the biomarker is underexpressed as compared to its level in the vesicles of normal subjects without the disease.

As described, the biosignature used to theranose a cancer can comprise analysis of one or more biomarker, which can be a protein or nucleic acid, including a mRNA or a microRNA. The biomarker can be detected in a bodily fluid and/or can be detected associated with a vesicle, e.g., as a vesicle antigen or as vesicle payload. In an illustrative example, the biosignature is used to identify a patient as a responder or non-responder to a tyrosine kinase inhibitor. The biomarkers can be one or more of those described in WO/2010/121238, entitled “METHODS AND KITS TO PREDICT THERAPEUTIC OUTCOME OF TYROSINE KINASE INHIBITORS” and filed Apr. 19, 2010; or WO/2009/105223, entitled “SYSTEMS AND METHODS OF CANCER STAGING AND TREATMENT” and filed Feb. 19, 2009; both of which applications are incorporated herein by reference in their entirety.

In an aspect, the present invention provides a method of determining whether a subject is likely to respond or not to a tyrosine kinase inhibitor, the method comprising identifying one or more biomarker in a vesicle population in a sample from the subject, wherein differential expression of the one or more biomarker in the sample as compared to a reference indicates that the subject is a responder or non-responder to the tyrosine kinase inhibitor. In an embodiment, the one or more biomarker comprises miR-497, wherein reduced expression of miR-497 indicates that the subject is a responder (i.e., sensitive to the tyrosine kinase inhibitor). In another embodiment, the one or more biomarker comprises one or more of miR-21, miR-23a, miR-23b, and miR-29b, wherein upregulation of the microRNA indicates that the subject is a likely non-responder (i.e., resistant to the tyrosine kinase inhibitor). In some embodiments, the one or more biomarker comprises onr or more of hsa-miR-029a, hsa-let-7d, hsa-miR-100, hsa-miR-1260, hsa-miR-025, hsa-let-71, hsa-miR-146a, hsa-miR-594-Pre, hsa-miR-024, FGFR1, MET, RAB25, EGFR, KIT and VEGFR2. In another embodiment, the one or more biomarker comprises FGF1, HOXC10 or LHFP, wherein higher expression of the biomarker indicates that the subject is a non-responder (i.e., resistant to the tyrosine kinase inhibitor). The method can be used to determine the sensitivity of a cancer to the tyrosine kinase inhibitor, e.g., a non-small cell lung cancer cell, kidney cancer or GIST. The tyrosine kinase inhibitor can be erlotinib, vandetanib, sunitinib and/or sorafenib, or other inhibitors that operate by a similar mechanism of action. A tyrosine kinase inhibitor includes any agent that inhibits the action of one or more tyrosine kinases in a specific or non-specific fashion. Tyrosine kinase inhibitors include small molecules, antibodies, peptides, or any appropriate entity that directly, indirectly, allosterically, or in any other way inhibits tyrosine residue phosphorylation. Specific examples of tyrosine kinase inhibitors include N-(trifluoromethylphenyl)-5-methylisoxazol-4-carboxamide, 3-[(2,4-dimethylpyrrol-5-yl)methylidenyl)indolin-2-one, 17-(allylamino)-17-demethoxygeldanamycin, 4-(3-chloro-4-fluorophenylamino)-7-methoxy-6-[3-(4-morpholinyl)propoxyl]q-uinazoline, N-(3-ethynylphenyl)-6,7-bis(2-methoxyethoxy)-4-quinazolinamine, BIBX1382, 2,3,9,10,11,12-hexahydro-10-(hydroxymethyl)-10-hydroxy-9-methyl-9,12-epox-y-1H-d{umlaut over (ν)}ndolo[1,2,3-fg:3′,2′,1′-k1]pyrrolo[3,4-i][1,6]benzodiazocin-1-one, SH268, genistein, STI571, CEP2563, 4-(3-chlorophenylamino)-5,6-dimethyl-7H-pyrrolo[2,3-d]pyrimidinemethane sulfonate, 4-(3-bromo-4-hydroxyphenyl)amino-6,7-dimethoxyquinazoline, 4-(4′-hydroxyphenyl)amino-6,7-dimethoxyquinazoline, SU6668, STI571A, N-4-chlorophenyl-4-(4-pyridylmethyl)-1-phthalazinamine, N-[2-(diethylamino)ethyl]-5-[(Z)-(5-fluoro-1,2-dihydro-2-oxo-3H-indol-3-ylidine)methyl]-2,4-dimethyl-1H-pyrrole-3-carboxamide (commonly known as sunitinib), A-[A-[[4-chloro-3 (trifluoromethyl)phenyl]carbamoylamino]phenoxy]-N-methyl-pyridine-2-carboxamide (commonly known as sorafenib), EMD121974, and N-(3-ethynylphenyl)-6,7-bis(2-methoxyethoxy)quinazolin-4-amine (commonly known as erlotinib). In some embodiments, the tyrosine kinase inhibitor has inhibitory activity upon the epidermal growth factor receptor (EGFR), VEGFR, PDGFR beta, and/or FLT3.

Thus, a treatment can be selected for the subject suffering from a cancer, based on a biosignature identified by the methods of the invention. Accordingly, the biosignature can comprise a presence or level of a circulating biomarker, including a microRNA, a vesicle, or any useful vesicle associated biomarker.

In some embodiments, a vesicle used to determine the sensitivity or resistance of a subject to a treatment, such as to predict if the subject will be a responder or non-responder to a treatment. For example, one or more biomarkers, such as ACTB, ACTN4, ADA, ADAM9, ADAMTS1, ADD 1, AF1Q, AIF1, AKAP1, AKAP13, AKR1C1, AKT1, ALDH2, ALDOC, ALG5, ALMS1, ALOX15B, AMIG02, AMPD2, AMPD3, ANAPC5, ANP32A, ANP32B, ANXA1, AP1G2, APOBEC3B, APRT, ARHE, ARHGAP15, ARHGAP25, ARHGDIB, ARHGEF6, ARL7, ASAH1, ASPH, ATF3, ATIC, ATP2A2, ATP2A3, ATP5D, ATP5G2, ATP6V1B2, BC008967, BCAT1, BCHE, BCL1 IB, BDNF, BHLHB2, BIN2, BLMH, BMI1, BNIP3, BRDT, BRRN1, BTN3A3, C1 1orf2, C14orf139, C15orf25, C18orflO, C1orf24, C1orf29, C1orf38, C1QR1, C22orf18, C6or02, CACNA1G, CACNB3, CALM1, CALML4, CALU, CAP350, CASP2, CASP6, CASP7, CAST, CBLB, CCNA2, CCNB1IP1, CCND3, CCR7, CCR9, CD1A, CD1C, CD1D, CD1E, CD2, CD28, CD3D, CD3E, CD3G, CD3Z, CD44, CD47, CD59, CD6, CD63, CD8A, CD8B1, CD99, CDC10, CDC 14B, CDHI 1, CDH2, CDKL5, CDKN2A, CDW52, CECR1, CENPB, CENTB1, CENTG2, CEP1, CG018, CHRNA3, CHS1, CIAPIN1, CKAP4, CKIP-I, CNP, COL4A1, COL5A2, COL6A1, CORO1C, CRABP1, CRK, CRY1, CSDA, CTBP1, CTSC, CTSL, CUGBP2, CUTC, CXCL1, CXCR4, CXorf9, CYFIP2, CYLD, CYR61, DAB2IP, DATF1, DAZAP1, DBN1, DBT, DCTN1, DDX1 8, DDX5, DGKA, DIAPH1, DKC1, DKFZP434J154, DKFZP564C186, DKFZP564G2022, DKFZp564J157, DKFZP564K0822, DNAJC10, DNAJC7, DNAPTP6, DOCK10, DOCK2, DPAGT1, DPEP2, DPYSL3, DSIPI, DUSP1, DXS9879E, EEF 1 B2, EFNB2, EHD2, EIF5A, ELK3, ENO2, EPAS1, EPB41L4B, ERCC2, ERG, ERP70, EVER1, EVI2A, EVL, EXT1, EZH2, F2R, FABP5, FAD1 04, FAM46A, FAU, FCGR2A, FCGR2c, FER1L3, FHL1, FHOD1, FKBP1A, FKBP9, FLJ1035O, FLJ10539, FLJ 10774, FLJ 12270, FLJ 13373, FLJ20859, FLJ21 159, FLJ22457, FLJ35036, FU46603, FLNC, FLOT1, FMNL1, FNBP1, FOLH1, FOXF2, FSCN1, FTL, FYB, FYN, G0S2, G6PD, GALIG, GALNT6, GATA2, GATA3, GFPT1, GIMAP5, GIT2, GJA1, GLRB, GLTSCR2, GLUL, GMDS, GNAQ, GNB2, GNB5, G0T2, GPR65, GPRASP1, GPSM3, GRP58, GSTM2, GTF3A, GTSE1, GZMA, GZMB, H1FO, H1FX, H2AFX, H3F₃A, HA-I, HEXB, HIC, HIST1H4C, HK1, HLA-A, HLA-B, HLA-DRA, HMGA1, HMGN2, HMMR, HNRPA1, HNRPD, HNRPM, HOXA9, HRMT1L1, HSA9761, HSPA5, HSU79274, HTATSF1, ICAM1, ICAM2, IER3, IFI1 6, IF144, IFITM2, IFITM3, IFRG28, IGFBP2, IGSF4, IL13RA2, IL21R, IL2RG, IL4R, IL6, IL6R, IL6ST, IL8, IMPDH2, INPP5D, INSIG1, IQGAP1, IQGAP2, IRS2, ITGA5, ITM2A, JARID2, JUNB, K-ALPHA-I, KHDRBS1, KIAA0355, KIAA0802, KIAA0877, KIAA0922, KIAA1078, KIAA1 128, KIAA1393, KIFC1, LAIR1, LAMB1, LAMB3, LAT, LBR, LCK, LCP1, LCP2, LEF1, LEPRE1, LGALS1, LGALS9, LHFPL2, LNK, LOC54103, LOC55831, LOC81558, LOC94105, LONP, LOX, LOXL2, LPHN2, LPXN, LRMP, LRP12, LRRC5, LRRN3, LST1, LTB, LUM, LY9, LY96, MAGEB2, MAL, MAP1B, MAP1LC3B, MAP4K1, MAPK1, MARCKS, MAZ, MCAM, MCL1, MCM5, MCM7, MDH2, MDN1, MEF2C, MFNG, MGC17330, MGC21654, MGC2744, MGC4083, MGC8721, MGC8902, MGLL, MLPH, MPHOSPH6, MPP1, MPZL1, MRP63, MRPS2, MT1E, MT1K, MUF1, MVP, MYB, MYL9, MYO1B, NAP1L1, NAP1 L2, NARF, NASP, NCOR2, NDN, NDUFAB1, NDUFS6, NFKBIA, NID2, NIPA2, NME4, NME7, NNMT, NOL5A, N0L8, N0M02, NOTCH1, NPC1, NQO1, NR1D2, NUDC, NUP210, NUP88, NVL, NXF1, OBFC1, OCRL, OGT, OXA1L, P2RX5, P4HA1, PACAP, PAF53, PAFAH1B3, PALM2-AKAP2, PAX6, PCBP2, PCCB, PFDN5, PFN1, PFN2, PGAM1, PHEMX, PHLDA1, PIM2, PITPNC1, PLACE, PLAGL1, PLAUR, PLCB1, PLEK2, PLEKHC1, PL0D2, PLSCR1, PNAS-4, PNMA2, P0LR2F, PPAP2B, PRF1, PRG1, PRIM1, PRKCH, PRKCQ, PRKD2, PRNP, PRP19, PRPF8, PRSS23, PSCDBP, PSMB9, PSMC3, PSME2, PTGER4, PTGES2, PTOV1, PTP4A3, PTPN7, PTPNS1, PTRF, PURA, PWP1, PYGL, QKI, RAB3GAP, RAB7L1, RAB9P40, RAC2, RAFTLIN, RAG2, RAP1B, RASGRP2, RBPMS, RCN1, RFC3, RFC5, RGC32, RGS3, RHOH, RIMS3, RI0K3, RIPK2, RIS1, RNASE6, RNF 144, RPL1O, RPL1OA, RPL12, RPL13 A, RPL1 7, RPL18, RPL36A, RPLPO, RPLP2, RPS15, RPS19, RPS2, RPS4X, RPS4Y1, RRAS, RRAS2, RRBP1, RRM2, RUNX1, RUNX3, S100A4, SART3, SATB1, SCAP1, SCARB1, SCN3A, SEC31L2, SEC61G, SELL, SELPLG, SEMA4G, SEPT10, SEPT6, SERPINA1, SERPINB1, SERPINB6, SFRS5, SFRS6, SFRS7, SH2D1A, SH3GL3, SH3TC1, SHD1, SHMT2, SIAT1, SKB1, SKP2, SLA, SLC1A4, SLC20A1, SLC25A15, SLC25A5, SLC39A14, SLC39A6, SLC43A3, SLC4A2, SLC7A1 1, SLC7A6, SMAD3, SMOX, SNRPA, SNRPB, SOD2, SOX4, SP140, SPANXC, SPI1, SRF, SRM, SSA2, SSBP2, SSRP1, SSSCA1, STAG3, STAT1, STAT4, STAT5A, STC1, STC2, STOML2, T3JAM, TACC1, TACC3, TAF5, TALI, TAP1, TARP, TBCA, TCF 12, TCF4, TFDP2, TFPI, TIMM 17 A, TIMP1, TJP1, TK2, TM4SF1, TM4SF2, TM4SF8, TM6SF1, TMEM2, TMEM22, TMSB1O, TMSNB, TNFAIP3, TNFAIP8, TNFRSF1OB, TNFRSF1A, TNFRSF7, TNIK, TNPO1, TOB1, TOMM20, TOX, TPK1, TPM2, TRA@, TRA1, TRAM2, TRB@, TRD@, TRIM, TRIM 14, TRIM22, TRIM28, TRIP13, TRPV2, TUBGCP3, TUSC3, TXN, TXNDC5, UBASH3A, UBE2A, UBE2L6, UBE2S, UCHL1, UCK2, UCP2, UFD1L, UGDH, ULK2, UMPS, UNG, USP34, USP4, VASP, VAV1, VLDLR, VWF, WASPIP, WBSCR20A, WBSCR20C, WHSC1, WNT5A, ZAP70, ZFP36L1, ZNF32, ZNF335, ZNF593, ZNFNIA1, ZYX, or a combination thereof, can be used to determine whether a subject is a responder or non-responder, or has sensitivity or resistance to radiation therapy or to one or more chemotherapy agents.

In another embodiment, one or more miRNAs, such as ath-MIR180aNo2, Hcd1O2 left, Hcd1 1 1 left, Hcd1 15 left, Hcd120 left, Hcd1 42 right, Hcd1 45 left, Hcd148_HPR225 left, Hcd1 81 left, Hcd1 81 right, Hcd210_HPR205 right, Hcd213_HPR182 left, Hcd230 left, Hcd243 right, Hcd246 right, Hcd248 right, Hcd249 right, Hcd250 left, Hcd255 left, Hcd257 left, Hcd257 right, Hcd263 left, Hcd266 left, Hcd270 right, Hcd279 left, Hcd279 right, Hcd28_HPR39left, Hcd28_HPR39right, Hcd282PO right, Hcd289 left, Hcd294 left, Hcd318 right, Hcd323 left, Hcd330 right, Hcd338 left, Hcd340 left, Hcd350 right, Hcd355_HPR190 left, Hcd361 right, Hcd366 left, Hcd373 right, Hcd383 left, Hcd383 right, Hcd384 left, Hcd397 left, Hcd404 left, Hcd412 left, Hcd413 right, Hcd415 right, Hcd417 right, Hcd421 right, Hcd425 left, Hcd43 Bright, Hcd434 right, Hcd438 left, Hcd440_HPR257 right, Hcd444 right, Hcd447 right, Hcd448 left, Hcd498 right, Hcd503 left, Hcd51 1 right, Hcd512 left, Hcd514 right, Hcd517 left, Hcd517 right, Hcd530 right, Hcd536_HPR104 right, Hcd542 left, Hcd544 left, Hcd547 left, Hcd559 right, Hcd562 right, Hcd569 right, Hcd570 right, Hcd578 right, Hcd581 right, Hcd586 left, Hcd586 right, Hcd587 right, Hcd605 left, Hcd605 left, Hcd605 right, Hcd608 right, Hcd627 left, Hcd631 left, Hcd631 right, Hcd634 left, Hcd642 right, Hcd649 right, Hcd654 left, Hcd658 right, Hcd669 right, Hcd674 left, Hcd678 right, Hcd683 left, Hcd684 right, Hcd689 right, Hcd690 right, Hcd691 right, Hcd693 right, Hcd697 right, Hcd704 left, Hcd704 left, Hcd712 right, Hcd716 right, Hcd731 left, Hcd738 left, Hcd739 right, Hcd739 right, Hcd749 right, Hcd753 left, Hcd754 left, Hcd755 left, Hcd760 left, Hcd763 right, Hcd768 left, Hcd768 right, Hcd770 left, Hcd773 left, Hcd777 left, Hcd778 right, Hcd781 left, Hcd781 right, Hcd782 left, Hcd783 left, Hcd788 left, Hcd794 right, Hcd796 left, Hcd799 left, Hcd807 right, Hcd812 left, Hcd817 left, Hcd817 right, Hcd829 right, Hcd852 right, Hcd861 right, Hcd863PO right, Hcd866 right, Hcd869 left, Hcd873 left, Hcd886 right, Hcd889 right, Hcd891 right, Hcd892 left, Hcd913 right, Hcd923 left, Hcd923 right, Hcd938 left, Hcd938 right, Hcd939 right, Hcd946 left, Hcd948 right, Hcd960 left, Hcd965 left, Hcd970 left, Hcd975 left, Hcd976 right, Hcd99 right, HPR100 right, HPR129 left, HPR154 left, HPR159 left, HPR163 left, HPR169 right, HPR172 right, HPR181 left, HPR187 left, HPR 199 right, HPR206 left, HPR213 right, HPR214 right, HPR220 left, HPR220 right, HPR227 right, HPR232 right, HPR233 right, HPR244 right, HPR262 left, HPR264 right, HPR266 right, HPR271 right, HPR76 right, hsa_mir_(—)490_Hcd20 right, HSHELAO1, HSTRNL, HUMTRAB, HUMTRF, HUMTRN, HUMTRS, HUMTRV1A, let-7f-2-prec2, mir-OO1b-1-prec1, mir-OO1b-2-prec, mir-007-1-prec, mir-007-2-precNo2, mir-O1Oa-precNo1, mir-015b-precNo2, mir-016a-chr13, mir-016b-chr3, mir-0,7-precNo1, mir-0,7-precNo2, mir-018-prec, mir-019a-prec, mir-019b-1-prec, mir-019b-2-prec, mir-020-prec, mir-022-prec, mir-023a-prec, mir-023b-prec, mir-024-2-prec, mir-025-prec, mir-027b-prec, mir-029c-prec, mir-032-precNo2, mir-033b-prec, mir-033-prec, mir-034-precNo1, mir-034-precNo2, mir-092-prec-13=092-1NO₂, mir-092-prec-X=092-2, mir-093-prec-7.1=093-1, mir-095-prec-4, mir-096-prec-7No1, mir-096-prec-7No2, mir-098-prec-X, mir-099b-prec-19No1, mir-100-1,2-prec, mir-1OONo1, mir-1O1-prec-9, mir-102-prec-1, mir-103-2-prec, mir-103-prec-5=103-1, mir-106No1, mir-106-prec-X, mir-107No1, mir-107-prec-10, mir-122a-prec, mir-123-precNo1, mir-123-precNo2, mir-124a-1-prec1, mir-124a-2-prec, mir-124a-3-prec, mir-125b-1, mir-125b-2-precNo2, mir-127-prec, mir-128b-precNo1, mir-128b-precNo2, mir-133a-1, mir-135-2-prec, mir-136-precNo2, mir-138-1-prec, mir-140No2, mir-142-prec, mir-143-prec, mir-144-precNo2, mir-145-prec, mir-146bNo1, mir-146-prec, mir-147-prec, mir-148No1, mir-148-prec, mir-149-prec, mir-150-prec, mir-153-1-prec1, mir-154-prec1No1, mir-155-prec, mir-15No1, mir-16-1No1, mir-16-2No1, mir-181a-precNo1, mir-181b-INoI, mir-181b-2No 1, mir-181b-precNo 1, mir-181b-precNo2, mir-181c-precNo1, mir-181dNo1, mir-188-prec, mir-18bNo2, mir-191-prec, mir-192No2, mir-193bNo2, mir-194-2No1, mir-195-prec, mir-196-2-precNo2, mir-197-prec, mir-198-prec, mir-199a-1-prec, mir-199a-2-prec, mir-199b-precNo1, mir-200a-prec, mir-200bNoI, mir-200bNo2, mir-202*, mir-202-prec, mir-204-precNo2, mir-205-prec, mir-208-prec, mir-20bNo1, mir-212-precNo1, mir-212-precNo2, mir-213-precNo1, mir-214-prec, mir-215-precNo2, mir-216-precNo1, mir-219-2No1, mir-219-prec, mir-223-prec, mir-29b-1No1, mir-29b-2=102prec7.1=7.2, mir-321No1, mir-321No2, mir-324NoI, mir-324No2, mir-328No1, mir-342No1, mir-361No1, mir-367No1, mir-370NoI, mir-371No1, miR-373*No1, mir-375, mir-376No1, mir-379No1, mir-380-5p, mir-382, mir-384, mir-409-3p, mir-423No1, mir-424No2, mir-429No1, mir-429No2, mir-4323p, mir-4325p, mir-449No1, mir-450-1, mir-450-2No1, mir-483No1, mir-484, mir-487No1, mir-495No1, mir-499No2, mir-501No2, mir-503No1, mir-509No1, mir-514-1No2, mir-515-15p, mir-515-23p, mir-516-33P, mir-516-43p, mir-518e/526c, mir-519a-1/52, mir-519a-2No2, mir-519b, mir-519c/52, mir-520c/52, mir-526a-2No1, mir-526a-2No2, MPR1 03 right, MPR121 left, MPR121 left, MPR130 left, MPR130 right, MPR133 right, MPR141 left, MPR151 left, MPR156 left, MPR162 left, MPR174 left, MPR174 right, MPR185 right, MPR197 right, MPR203 left, MPR207 right, MPR215 left, MPR216 left, MPR224 left, MPR224 right, MPR228 left, MPR234 right, MPR237 left, MPR243 left, MPR244 right, MPR249 left, MPR254 right, MPR74 left, MPR88 right, MPR95 1, or a combination thereof, is assessed in a membrane vesicle from a subject, and used to determine whether a subject is a responder or non-responder, or has sensitivity or resistance to radiation therapy or to chemotherapy agents.

In one embodiment, the level of expression of the one or more of biomarkers can be measured for one or more biomarkers. A change in the level of expression of the biomarker can indicate a subject is sensitive or resistant to a treatment known to change. For example, an increase or decrease can be detected and compared to a control sample. Based on the increase or decrease, the subject can be determined to be sensitive, or likely to be sensitive, to treatment with an agent. For example, the change in expression may be of a biomarker that has been known or previously determined to change in a patient that is a responder to a treatment. Thus, detecting a change in the biomarker expression for a subject can indicate that the subject is also a responder. Alternatively, a change in expression may be of a biomarker that has been known or previously determined to change in a patient that is a non-responder to a treatment. Thus, detecting a change in the biomarker expression for a subject can indicate that the subject is also a non-responder.

The treatment can comprise one or more chemotherapy agents such as, but not limited to, Vincristine, Cisplatin, Adriamycin, Etoposide, Azaguanine, Aclarubicin, Mitoxantrone, Paclitaxel, Mitomycin, Gemcitabine, Taxotere, Dexamethasone, Methylprednisolone, Ara-C, Methotrexate, Bleomycin, Methyl-GAG, Rituximab, PXD1O1 (a histone deacetylase (HDAC) inhibitor), 5-Aza-2′-deoxycytidine (Decitabine), Melphalan, IL4-PE38 fusion protein, IL13-PE38QQR fusion protein (cintredekin besudotox), Valproic acid (VPA), All-trans retinoic acid (ATRA), Cytoxan, Topotecan (Hycamtin), Suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza), Depsipeptide (FR901229), Bortezomib, Leukeran, Fludarabine, Vinblastine, Busulfan, Dacarbazine, Oxaliplatin, Hydroxyurea, Tegafur, Daunorubicin, Bleomycin, Estramustine, Chlorambucil, Mechlorethamine, Streptozocin, Carmustine, Lomustine, Mercaptopurine, Teniposide, Dactinomycin, Tretinoin, Sunitinib, SPC2996, Ifosfamide, Tamoxifen, Floxuridine, Irinotecan, Satraplatin, or a combination thereof.

In some embodiments, the therapy comprises Azaguanine, Etoposide, Paclitaxel, Gemcitabine, Taxotere, Dexamethasone, Ara-C, Methylprednisolone, Methotrexate, Bleomycin, Methyl-GAG, Carboplatin, 5-FU (5-Fluorouracil), a histone deacetylase (HDAC) inhibitor such as PXD1O1, 5-Aza-2′-deoxycytidine (Decitabine), alpha emitters such as astatine-211, bismuth-212, bismuth-213, lead-212, radium-223, actinium-225, and thorium-227, beta emitters such as tritium, strontium-90, cesium-137, carbon-1 1, nitrogen-13, oxygen-15, fluorine-18, iron-52, cobalt-55, cobalt-60, copper-61, copper-62, copper-64, zinc-62, zinc-63, arsenic-70, arsenic-71, arsenic-74, bromine-76, bromine-79, rubidium-82, yttrium-86, zirconium-89, indium-1 10, iodine-120, iodine-124, iodine-129, iodine-131, iodine-125, xenon-122, technetium-94m, technetium-94, technetium-99m, and technetium-99, gamma emitters such as cobalt-60, cesium-137, and technetium-99m, Alemtuzumab, Daclizumab, Rituximab (e.g., MABTHERA™), Trastuzumab (e.g., HERCEPTIN™), Gemtuzumab, Ibritumomab, Edrecolomab, Tositumomab, CeaVac, Epratuzumab, Mitumomab, Bevacizumab, Cetuximab, Edrecolomab, Lintuzumab, MDX-210, IGN-101, MDX-010, MAb, AME, ABX-EGF, EMD 72 000, Apolizumab, Labetuzumab, ior-t1, MDX-220, MRA, H-1 1 scFv, Oregovomab, huJ591 MAb, BZL, Visilizumab, TriGem, TriAb, R3, MT-201, G-250, unconjugated, ACA-125, Onyvax-105, CDP-860, BrevaRex MAb, AR54, IMC-IC1 1, GHoMAb-H, ING-I 5 Anti-LCG MAbs, MT-103, KSB-303, Therex, KW-2871, Anti-HMI.24, Anti-PTHrP, 2C4 antibody, SGN-30, TRAIL-R1MAb, CAT, Prostate cancer antibody, H22xKi-4, ABX-MA1, Imuteran, Monopharm-C, Acivicin, Aclarubicin, Acodazole Hydrochloride, Acronine, Adozelesin, Adriamycin, Aldesleukin, Altretamine, Ambomycin, A. metantrone Acetate, Aminoglutethimide, Amsacrine, Anastrozole, Anthramycin, Asparaginase, Asperlin, Azacitidine, Azetepa, Azotomycin, Batimastat, Benzodepa, Bicalutamide, Bisantrene Hydrochloride, Bisnafide Dimesylate, Bizelesin, Bleomycin Sulfate, Brequinar Sodium, Bropirimine, Busulfan, Cactinomycin, Calusterone, Camptothecin, Caracemide, Carbetimer, Carboplatin, Carmustine, Carubicin Hydrochloride, Carzelesin, Cedefingol, Chlorambucil, Cirolemycin, Cisplatin, Cladribine, Combretestatin A-4, Crisnatol Mesylate, Cyclophosphamide, Cytarabine, Dacarbazine, DACA (N-[2-(Dimethyl-amino)ethyl]acridine-4-carboxamide), Dactinomycin, Daunorubicin Hydrochloride, Daunomycin, Decitabine, Dexormaplatin, Dezaguanine, Dezaguanine Mesylate, Diaziquone, Docetaxel, Dolasatins, Doxorubicin, Doxorubicin Hydrochloride, Droloxifene, Droloxifene Citrate, Dromostanolone Propionate, Duazomycin, Edatrexate, Eflornithine Hydrochloride, Ellipticine, Elsamitrucin, Enloplatin, Enpromate, Epipropidine, Epirubicin Hydrochloride, Erbulozole, Esorubicin Hydrochloride, Estramustine, Estramustine Phosphate Sodium, Etanidazole, Ethiodized Oil 1 131, Etoposide, Etoposide Phosphate, Etoprine, Fadrozole Hydrochloride, Fazarabine, Fenretinide, Floxuridine, Fludarabine Phosphate, Fluorouracil, 5-FdUMP, Fluorocitabine, Fosquidone, Fostriecin Sodium, Gemcitabine, Gemcitabine Hydrochloride, Gold Au 198, Homocamptothecin, Hydroxyurea, Idarubicin Hydrochloride, Ifosfamide, Ilmofosine, Interferon Alfa-2a, Interferon Alfa-2b, Interferon Alfa-n1, Interferon Alfa-n3, Interferon Beta-I a, Interferon Gamma-I b, Iproplatin, Irinotecan Hydrochloride, Lanreotide Acetate, Letrozole, Leuprolide Acetate, Liarozole Hydrochloride, Lometrexol Sodium, Lomustine, Losoxantrone Hydrochloride, Masoprocol, Maytansine, Mechlorethamine Hydrochloride, Megestrol Acetate, Melengestrol Acetate, Melphalan, Menogaril, Mercaptopurine, Methotrexate, Methotrexate Sodium, Metoprine, Meturedepa, Mitindomide, Mitocarcin, Mitocromin, Mitogillin, Mitomalcin, Mitomycin, Mitosper, Mitotane, Mitoxantrone Hydrochloride, Mycophenolic Acid, Nocodazole, Nogalamycin, Ormaplatin, Oxisuran, Paclitaxel, Pegaspargase, Peliomycin, Pentamustine, PeploycinSulfate, Perfosfamide, Pipobroman, Piposulfan, Piroxantrone Hydrochloride, Plicamycin, Plomestane, Porfimer Sodium, Porfiromycin, Prednimustine, Procarbazine Hydrochloride, Puromycin, Puromycin Hydrochloride, Pyrazofurin, Rhizoxin, Rhizoxin D, Riboprine, Rogletimide, Safingol, Safingol Hydrochloride, Semustine, Simtrazene, Sparfosate Sodium, Sparsomycin, Spirogermanium Hydrochloride, Spiromustine, Spiroplatin, Streptonigrin, Streptozocin, Strontium Chloride Sr 89, Sulofenur, Talisomycin, Taxane, Taxoid, Tecogalan Sodium, Tegafur, Teloxantrone Hydrochloride, Temoporfin, Teniposide, Teroxirone, Testolactone, Thiamiprine, Thioguanine, Thiotepa, Thymitaq, Tiazofurin, Tirapazamine, Tomudex, TOP53, Topotecan Hydrochloride, Toremifene Citrate, Trestolone Acetate, Triciribine Phosphate, Trimetrexate, Trimetrexate Glucuronate, Triptorelin, Tubulozole Hydrochloride, Uracil Mustard, Uredepa, Vapreotide, Verteporfin, Vinblastine, Vinblastine Sulfate, Vincristine, Vincristine Sulfate, Vindesine, Vindesine Sulfate, Vinepidine Sulfate, Vinglycinate Sulfate, Vinleurosine Sulfate, Vinorelbine Tartrate, Vinrosidine Sulfate, Vinzolidine Sulfate, Vorozole, Zeniplatin, Zinostatin, Zorubicin Hydrochloride, 2-Chlorodeoxyadenosine, 2′ Deoxyformycin, 9-aminocamptothecin, raltitrexed, N-propargyl-5,8-dideazafolic acid, 2-chloro-2′-arabino-fluoro-2′-deoxyadenosine, 2-chloro-2′-deoxyadenosine, anisomycin, trichostatin A, hPRL-G129R, CEP-751, linomide, sulfur mustard, nitrogen mustard (mechlor ethamine), cyclophosphamide, melphalan, chlorambucil, ifosfamide, busulfan, N-methyl-Nnitrosourea (MNU), N,N′-Bis (2-chloroethyl)-N-nitrosourea (BCNU), N-(2-chloroethyl)-N′ cyclohexyl-N-nitrosourea (CCNU), N-(2-chloroethyl)-N′-(trans-4-methylcyclohexyl-N-nitrosourea (MeCCNU), N-(2-chloroethyl)-N-(diethyl)ethylphosphonate-N-nitrosourea (fotemustine), streptozotocin, diacarbazine (DTIC), mitozolomide, temozolomide, thiotepa, mitomycin C, AZQ, adozelesin, Cisplatin, Carboplatin, Ormaplatin, Oxaliplatin, C 1-973, DWA 21 14R, JM216, JM335, Bis (platinum), tomudex, azacitidine, cytarabine, gemcitabine, 6-Mercaptopurine, 6-Thioguanine, Hypoxanthine, teniposide 9-amino camptothecin, Topotecan, CPT-1 1, Doxorubicin, Daunomycin, Epirubicin, darubicin, mitoxantrone, losoxantrone, Dactinomycin (Actinomycin D), amsacrine, pyrazoloacridine, all-trans retinol, 14-hydroxy-retro-retinol, all-trans retinoic acid, N-(4-Hydroxyphenyl) retinamide, 13-cis retinoic acid, 3-Methyl TTNEB, 9-cis retinoic acid, fludarabine (2-F-ara-AMP), 2-chlorodeoxyadenosine (2-Cda), 20-pi-1,25 dihydroxyvitamin D3, 5-ethynyluracil, abiraterone, aclarubicin, acylfulvene, adecypenol, adozelesin, aldesleukin, ALL-TK antagonists, altretamine, ambamustine, amidox, amifostine, aminolevulinic acid, amrubicin, amsacrine, anagrelide, anastrozole, andrographolide, angiogenesis inhibitors, antagonist D, antagonist G, antarelix, anti-dorsalizing morphogenetic protein-1, antiandrogen, prostatic carcinoma, antiestrogen, antineoplaston, antisense oligonucleotides, aphidicolin glycinate, apoptosis gene modulators, apoptosis regulators, apurinic acid, ara-CDP-DL-PTBA, argininedeaminase, asulacrine, atamestane, atrimustine, axinastatin 1, axinastatin 2, axinastatin 3, azasetron, azatoxin, azatyrosine, baccatin III derivatives, balanol, batimastat, BCR/ABL antagonists, benzochlorins, benzoylstaurosporine, beta lactam derivatives, beta-alethine, betaclamycin B, betulinic acid, bFGF inhibitor, bicalutamide, bisantrene, bisaziridinylspermine, bisnafide, bistratene A, bizelesin, breflate, bleomycin A2, bleomycin B2, bropirimine, budotitane, buthionine sulfoximine, calcipotriol, calphostin C, camptothecin derivatives (e.g., 10-hydroxy-camptothecin), canarypox IL-2, capecitabine, carboxamide-amino-triazole, carboxyamidotriazole, CaRest M3, CARN 700, cartilage derived inhibitor, carzelesin, casein kinase inhibitors (ICOS), castanospermine, cecropin B, cetrorelix, chlorins, chloroquinoxaline sulfonamide, cicaprost, cis-porphyrin, cladribine, clomifene analogues, clotrimazole, collismycin A, collismycin B, combretastatin A4, combretastatin analogue, conagenin, crambescidin 816, crisnatol, cryptophycin 8, cryptophycin A derivatives, curacin A, cyclopentanthraquinones, cycloplatam, cypemycin, cytarabine ocfosfate, cytolytic factor, cytostatin, dacliximab, decitabine, dehydrodidemnin B, 2′ deoxycoformycin (DCF), deslorelin, dexifosfamide, dexrazoxane, dexverapamil, diaziquone, didemnin B, didox, diethylnorspermine, dihydro-5-azacytidine, 9-dihydrotaxol, dioxamycin, diphenyl spiromustine, discodermolide, docosanol, dolasetron, doxifluridine, droloxifene, dronabinol, duocarmycin SA, ebselen, ecomustine, edelfosine, edrecolomab, eflornithine, elemene, emitefur, epirubicin, epothilones (A, R=H, B, R=Me), epithilones, epristeride, estramustine analogue, estrogen agonists, estrogen antagonists, etanidazole, etoposide, etoposide 4′-phosphate (etopofos), exemestane, fadrozole, fazarabine, fenretinide, filgrastim, finasteride, flavopiridol, flezelastine, fluasterone, fludarabine, fluorodaunorunicin hydrochloride, forfenimex, formestane, fostriecin, fotemustine, gadolinium texaphyrin, gallium nitrate, galocitabine, ganirelix, gelatinase inhibitors, gemcitabine, glutathione inhibitors, hepsulfam, heregulin, hexamethylene bisacetamide, homoharringtonine (HHT), hypericin, ibandronic acid, idarubicin, idoxifene, idramantone, ilmofosine, ilomastat, imidazoacridones, imiquimod, immunostimulant peptides, insulin-like growth factor-1 receptor inhibitor, interferon agonists, interferons, interleukins, iobenguane, iododoxorubicin, 4-ipomeanol, irinotecan, iroplact, irsogladine, isobengazole, isohomohalicondrin B, itasetron, jasplakinolide, kahalalide F, lamellarin-N triacetate, lanreotide, leinamycin, lenograstim, lentinan sulfate, leptolstatin, letrozole, leukemia inhibiting factor, leukocyte alpha interferon, leuprolide, estrogen, and progesterone combinations, leuprorelin, levamisole, liarozole, linear polyamine analogue, lipophilic disaccharide peptide, lipophilic platinum compounds, lissoclinamide 7, lobaplatin, lombricine, lometrexol, lonidamine, losoxantrone, lovastatin, loxoribine, lurtotecan, lutetium texaphyrin, lysofylline, lytic peptides, maytansine, mannostatin A, marimastat, masoprocol, maspin, matrilysin inhibitors, matrix metalloproteinase inhibitors, menogaril, merbarone, meterelin, methioninase, metoclopramide, MIF inhibitor, ifepristone, miltefosine, mirimostim, mismatched double stranded RNA, mithracin, mitoguazone, mitolactol, mitomycin analogues, mitonafide, mitotoxin fibroblast growth factor-saporin, mitoxantrone, mofarotene, molgramostim, monoclonal antibody, human chorionic gonadotrophin, monophosphoryl lipid A and myobacterium cell wall skeleton combinations, mopidamol, multiple drug resistance gene inhibitor, multiple tumor suppressor 1-based therapy, mustard anticancer agent, mycaperoxide B, mycobacterial cell wall extract, myriaporone, N-acetyldinaline, N-substituted benzamides, nafarelin, nagrestip, naloxone and pentazocine combinations, napavin, naphterpin, nartograstim, nedaplatin, nemorubicin, neridronic acid, neutral endopeptidase, nilutamide, nisamycin, nitric oxide modulators, nitroxide antioxidant, nitrullyn, 06-benzylguanine, octreotide, okicenone, oligonucleotides, onapristone, ondansetron, ondansetron, oracin, oral cytokine inducer, ormaplatin, osaterone, oxaliplatin, oxaunomycin, paclitaxel analogues, paclitaxel derivatives, palauamine, palmitoylrhizoxin, pamidronic acid, panaxytriol, panomifene, parabactin, pazelliptine, pegaspargase, peldesine, pentosan polysulfate sodium, pentostatin, pentrozole, perflubron, perfosfamide, perillyl alcohol, phenazinomycin, phenylacetate, phosphatase inhibitors, picibanil, pilocarpine hydrochloride, pirarubicin, piritrexim, placetin A, placetin B, plasminogen activator inhibitor, platinum complex, platinum compounds, platinum-triamine complex, podophyllotoxin, porfimer sodium, porfiromycin, propyl bis-acridone, prostaglandin 32, proteasome inhibitors, protein A-based immune modulator, protein kinase C inhibitor, protein kinase C inhibitors, microalgal, protein tyrosine phosphatase inhibitors, purine nucleoside phosphorylase inhibitors, purpurins, pyrazoloacridine, pyridoxylated hemoglobin polyoxyethylene conjugate, raf antagonists, raltitrexed, ramosetron, ras farnesyl protein transferase inhibitors, ras inhibitors, ras-GAP inhibitor, retelliptine demethylated, rhenium Re 186 etidronate, rhizoxin, ribozymes, RII retinamide, rogletimide, rohitukine, romurtide, roquinimex, rubiginone B 1, ruboxyl, safingol, saintopin, SarCNU, sarcophytol A, sargramostim, Sdi 1 mimetics, semustine, senescence derived inhibitor 1, sense oligonucleotides, signal transduction inhibitors, signal transduction modulators, single chain antigen binding protein, sizofuran, sobuzoxane, sodium borocaptate, sodium phenylacetate, solverol, somatomedin binding protein, sonermin, sparfosic acid, spicamycin D, spiromustine, splenopentin, spongistatin 1, squalamine, stem cell inhibitor, stem-cell division inhibitors, stipiamide, stromelysin inhibitors, sulfinosine, superactive vasoactive intestinal peptide antagonist, suradista, suramin, swainsonine, synthetic glycosaminoglycans, tallimustine, tamoxifen methiodide, tauromustine, tazarotene, tecogalan sodium, tegafur, tellurapyrylium, telomerase inhibitors, temoporfin, temozolomide, teniposide, tetrachlorodecaoxide, tetrazomine, thaliblastine, thalidomide, thiocoraline, thrombopoietin, thrombopoietin mimetic, thymalfasin, thymopoietin receptor agonist, thymotrinan, thyroid stimulating hormone, tin ethyl etiopurpurin, tirapazamine, titanocene dichloride, topotecan, topsentin, toremifene, totipotent stem cell factor, translation inhibitors, tretinoin, triacetyluridine, triciribine, trimetrexate, triptorelin, tropisetron, turosteride, tyrosine kinase inhibitors, tyrphostins, UBC inhibitors, ubenimex, urogenital sinus-derived growth inhibitory factor, urokinase receptor antagonists, vapreotide, variolin B, vector system, erythrocyte gene therapy, velaresol, veramine, verdins, verteporfin, vinorelbine, vinxaltine, vitaxin, vorozole, zanoterone, zeniplatin, zilascorb, zinostatin stimalamer, or a combination thereof.

In one embodiment, one or more of the biomarkers SFRS3, CCT5, RPL39, SLC25A5, UBE2S, EEF1A1, RPLP2, RPL24, RPS23, RPL39, RPL18, NCL, RPL9, RPL1OA, RPS10, EIF3S2, SHFM1, RPS28, REA, RPL36A, GAPD, HNRPA1, RPSI 1, HNRPA1, LDHB, RPL3, RPL1 1, MRPL12, RPL 18 A, COX7B, RPS7, or a combination thereof, preferably gene sequences UBB, RPS4X, S100A4, NDUFS6, B2M, C14orf139, MAN1A1, SLC25A5, RPL1O, RPL12, EIF5A, RPL36A, SUI1, BLMH, CTBP1, TBCA, MDH2, DXS9879E, or a combination thereof, and most preferably one or more of the biomarkers RPS4X, S100A4, NDUFS6, C14orf139, SLC25A5, RPL1O, RPL12, EIF5A, RPL36A, BLMH, CTBP1, TBCA, MDH2, DXS9879E, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Vincristine.

In another embodiment, one or more of the biomarkers B2M, ARHGDIB, FTL, NCL, MSN, SNRPF, XPO1, LDHB, SNRPF, GAPD, PTPN7, ARHGDIB, RPS27, IFI 16, C5orf13, HCLS1, or a combination thereof, preferably biomarkers C1QR1, HCLS1, CD53, SLA, PTPN7, PTPRCAP, ZNFNIA1, CENTB1, PTPRC, IFI 16, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRF1, ARHGAP15, TM6SF1, TCF4, or a combination thereof, and most preferably biomarkers C1QR1, SLA, PTPN7, ZNFNIA1, CENTB1, IFI 16, ARHGEF6, SEC31L2, CD3Z, GZMB, CD3D, MAP4K1, GPR65, PRF1, ARHGAP1 5, TM6SF1, TCF4, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Cisplatin.

In one embodiment, one or more of the biomarkers PRPS1, DDOST, B2M, SPARC, LGALS 1, CBFB, SNRPB2, MCAM, MCAM, EIF2S2, HPRT1, SRM, FKBP1A, GYPC, UROD, MSN, HNRPA1, SND1, COPA, MAPRE1, EIF3S2, ATP1B3, EMP3, ECM1, ATOX1, NARS, PGK1, OK/SW-c1.56, FN1, EEF1A1, GNAI2, PRPS1, RPL7, PSMB9, GPNMB, PPP1R1 1, MIA, RAB7, VIM, and SMS, preferably biomarkers MSN, SPARC, VIM, SRM, SCARB1, SIAT1, CUGBP2, GAS7, ICAM1, WASPIP, ITM2A, PALM2-AKAP2, ANPEP, PTPNS1, MPP1, LNK, FCGR2A, EMP3, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCP1, IFI16, MCAM, MEF2C, SLC1A4, BTN3A2, FYN, FN1, C1orf38, CHS1, CAPN3, FCGR2c, TNIK, AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-I, FLJ10539, FLJ35036, DOCK10, TRPV2, IFRG28, LEF1, ADAMTS1, or a combination thereof, and most preferably biomarkers SRM, SCARB1, SIAT1, CUGBP2, ICAM1, WASPIP, ITM2A, PALM2-AKAP2, PTPNS1, MPP1, LNK, FCGR2A, RUNX3, EVI2A, BTN3A3, LCP2, BCHE, LY96, LCP1, IFI1 6, MCAM, MEF2C, SLC 1A4, FYN, C1orOδ, CHS1, FCGR2c, TNIK, AMPD2, SEPT6, RAFTLIN, SLC43A3, RAC2, LPXN, CKIP-I, FLJ10539, FLJ35036, DOCK10, TRPV2, IFRG28, LEF1, ADAMTS1, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Azaguanine.

In one embodiment, one or more of the biomarkers B2M, MYC, CD99, RPS24, PPIF, PBEF 1, ANP32B, or a combination thereof, preferably biomarkers CD99, INSIG1, LAPTM5, PRG1, MUF1, HCLS1, CD53, SLA, SSBP2, GNB5, MFNG, GMFG, PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, PTPRCAP, ZNFN1A1, CENTB1, PTPRC, NAP1L1, HLA-DRA, IFI 16, CORO1A, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, ITK, RAFTLIN, DOCK2, CD3D, RAC2, ZAP70, GPR65, PRF1, ARHGAP15, NOTCH1, UBASH3A, or a combination thereof, and most preferably biomarkers CD99, INSIG1, PRG1, MUF1, SLA, SSBP2, GNB5, MFNG, PSMB9, EVI2A, PTPN7, PTGER4, CXorf9, ZNFN1A1, CENTB1, NAP1L1, HLA-DRA, IFI 16, ARHGEF6, PSCDBP, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, GZMB, SCN3A, RAFTLIN, DOCK2, CD3D, RAC2, ZAP70, GPR65, PRF1, ARHGAP 15, NOTCH1, UBASH3A, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Etoposide.

In one embodiment, one or more of the biomarkers KIAA0220, B2M, TOP2A, CD99, SNRPE, RPS27, HNRPA1, CBX3, ANP32B, HNRPA1, DDX5, PPIA, SNRPF, USP7, or a combination thereof, preferably biomarkers CD99, LAPTM5, ALDOC, HCLS1, CD53, SLA, SSBP2, IL2RG, GMFG, CXorf9, RHOH, PTPRCAP, ZNFN1A1, CENTB1, TCF7, CD1C, MAP4K1, CD1B, CD3G, PTPRC, CCR9, CORO1A, CXCR4, ARHGEF6, HEM1, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CD1A, LAIR1, ITK, TRB@, CD3D, WBSCR20C, ZAP70, IF144, GPR65, AIF1, ARHGAP15, NARF, PACAP, or a combination thereof, and most preferably biomarkers CD99, ALDOC, SLA, SSBP2, IL2RG, CXorf9, RHOH, ZNFNIA1, CENTB1, CD1C, MAP4K1, CD3G, CCR9, CXCR4, ARHGEF6, SELPLG, LAT, SEC31L2, CD3Z, SH2D1A, CD1A, LAIR1, TRB@, CD3D, WBSCR20C, ZAP70, IF144, GPR65, AIF1, ARHGAP 15, NARF, PACAP, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Adriamycin.

In one embodiment, one or more of the biomarkers RPLP2, LAMR1, RPS25, EIF5A, TUFM, HNRPA1, RPS9, MYB, LAMR1, ANP32B, HNRPA1, HNRPA1, EIF4B, HMGB2, RPS15 A, RPS7, or a combination thereof, preferably biomarkers RPL12, RPL32, RPLP2, MYB, ZNFNIA1, SCAP1, STAT4, SP140, AMPD3, TNFAIP8, DDX18, TAF5, FBL, RPS2, PTPRC, DOCK2, GPR65, HOXA9, FLJ 12270, HNRPD, or a combination thereof, and most preferably biomarkers RPL 12, RPLP2, MYB, ZNFN1A1, SCAP1, STAT4, SP 140, AMPD3, TNFAIP8, DDX 18, TAF5, RPS2, DOCK2, GPR65, HOXA9, FLJ 12270, HNRPD, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Aclarubicin.

In one embodiment, one or more of the biomarkers ARHGEF6, B2M, TOP2A, TOP2A, ELA2B, PTMA, LMNB1, TNFRSF1A, NAP1L1, B2M, HNRPA1, RPL9, C5orf13, NCOR2, ANP32B, OK/SW-c1.56, TUBA3, HMGN2, PRPS1, DDX5, PRG1, PPIA, G6PD, PSMB9, SNRPF, MAP1B, or a combination thereof, preferably biomarkers PGAM1, DPYSL3, INSIG1, GJA1, BNIP3, PRG1, G6PD, BASP1, PLOD2, LOXL2, SSBP2, C1orf29, TOX, STC1, TNFRSF1A, NCOR2, NAP1L1, LOC94105, COL6A2, ARHGEF6, GAT A3, TFPI, LAT, CD3Z, AF1Q, MAP1B, PTPRC, PRKCA, TRIM22, CD3D, BCAT1, IF144, CCL2, RAB31, CUTC, NAP1L2, NME7, FLJ21 159, COL5A2, or a combination thereof, and most preferably biomarkers PGAM1, DPYSL3, INSIG1, GJA1, BNIP3, PRG1, G6PD, PLOD2, LOXL2, SSBP2, C1orf29, TOX, STC1, TNFRSF1A, NCOR2, NAP1L1, LOC94105, ARHGEF6, GATA3, TFPI, LAT, CD3Z, AF1Q, MAP1B, TRIM22, CD3D, BCAT1, IF144, CUTC, NAP1L2, NME7, FLJ21 159, COL5A2, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Mitoxantrone.

In one embodiment, one or more of the biomarkers GAPD, GAPD, GAPD, TOP2A, SUI1, TOP2A, FTL, HNRPC, TNFRSF1A, SHC1, CCT7, P4HB, CTSL, DDX5, G6PD, SNRPF, or a combination thereof, preferably biomarkers STC1, GPR65, DOCK10, COL5A2, FAM46A, LOC54103, or a combination thereof, and most preferably biomarkers STC1, GPR65, DOCK10, COL5A2, FAM46A, LOC54103, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Mitomycin.

In one embodiment, one or more of the biomarkers RPS23, SFRS3, KIAAO1 14, RPL39, SFRS3, LOC51035, RPS6, EXOSC2, RPL35, IFRD2, SMN2, EEF1A1, RPS3, RPS18, RPS7, or a combination thereof, preferably biomarkers RPL1O, RPS4X, NUDC, RALY, DKC1, DKFZP564C186, PRP 19, RAB9P40, HSA9761, GMDS, CEP1, IL13RA2, MAGEB2, HMGN2, ALMS1, GPR65, FLJ10774, NOL8, DAZAP1, SLC25A15, PAF53, DXS9879E, PITPNC1, SPANXC, and KIAA1393, and most preferably biomarkers RPL1O, RPS4X, NUDC, DKC1, DKFZP564C186, PRP 19, RAB9P40, HSA9761, GMDS, CEP1, IL13RA2, MAGEB2, HMGN2, ALMS1, GPR65, FLJ 10774, NOL8, DAZAP1, SLC25A15, PAF53, DXS9879E, PITPNC1, SPANXC, KIAA1393, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Paclitaxel.

In one embodiment, one or more of the biomarkers CSDA, LAMR1, TUBA3, or a combination thereof, preferably biomarkers PFN1, PGAM1, K-ALPHA-I, CSDA, UCHL1, PWP1, PALM2, AKAP2, TNFRSF1A, ATP5G2, AF1Q, NME4, FHOD1, or a combination thereof, and most preferably biomarkers PFN1, PGAM1, K-ALPHA-I, CSDA, UCHL1, PWP1, PALM2-AKAP2, TNFRSF1A, ATP5G2, AF1Q, NME4, FHOD1, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Gemcitabine.

In one embodiment, one or more of the biomarkers RPS23, SFRS3, KIAAO114, SFRS3, RPS6, DDX39, RPS7, or a combination thereof, preferably biomarkers ANP32B, GTF3A, RRM2, TRIM 14, SKP2, TRIP13, RFC3, CASP7, TXN, MCM5, PTGES2, OBFC1, EPB41L4B, CALML4, or a combination thereof, and most preferably biomarkers ANP32B, GTF3A, RRM2, TRIM 14, SKP2, TRIP13, RFC3, CASP7, TXN, MCM5, PTGES2, OBFC1, EPB41L4B, CALML4, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Taxotere.

In one embodiment, one or more of the biomarkers IL2RG, H1 FX, RDBP, ZAP70, CXCR4, TM4SF2, ARHGDIB, CDA, CD3E, STMN1, GNA15, AXL, CCND3, SATB1, EIF5A, LCK, NKX2-5, LAPTM5, IQGAP2, FLII, EIF3S5, TRB, CD3D, HOXB2, GATA3, HMGB2, PSMB9, ATP5G2, CORO1A, ARHGDIB, DRAP1, PTPRCAP, RHOH, ATP2A3, or a combination thereof, preferably biomarkers IFITM2, UBE2L6, LAPTM5, USP4, ITM2A, ITGB2, ANPEP, CD53, IL2RG, CD37, GPRASP1, PTPN7, CXorf9, RHOH, GIT2, AD0RA2A, ZNFN1A1, GNA15, CEP1, TNFRSF7, MAP4K1, CCR7, CD3G, PTPRC, ATP2A3, UCP2, CORO1A, GATA3, CDKN2A, HEM1, TARP, LAIR1, SH2D1A, FLII, SEPT6, HA-I, CREB3L1, ERCC2, CD3D, LST1, AIF1, ADA, DATF1, ARHGAP15, PLAC8, CECR1, LOC81558, EHD2, or a combination thereof, and most preferably biomarkers IFITM2, UBE2L6, USP4, ITM2A, IL2RG, GPRASP1, PTPN7, CXorf9, RHOH, GIT2, ZNFN1A1, CEP1, TNFRSF7, MAP4K1, CCR7, CD3G, ATP2A3, UCP2, GATA3, CDKN2A, TARP, LAIR1, SH2D1A, SEPT6, HA-I, ERCC2, CD3D, LST1, AIF1, ADA, DATF1, ARHGAP15, PLAC8, CECR1, LOC81558, EHD2, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Dexamethasone.

In one embodiment, one or more of the biomarkers TM4SF2, ARHGDIB, ADA, H2 AFZ, NAP1L1, CCND3, FABP5, LAMR1, REA, MCM5, SNRPF, USP7, or a combination thereof, preferably biomarkers ITM2A, RHOH, PRIM1, CENTB1, GNA15, NAP1L1, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, PTPRC, NME4, RPL13, CD3D, CD1E, ADA, FHOD1, and most preferably biomarkers ITM2A, RHOH, PRIM1, CENTB1, NAP1L1, ATP5G2, GATA3, PRKCQ, SH2D1A, SEPT6, NME4, CD3D, CD1E, ADA, FHOD1, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Ara-C.

In one embodiment, one or more of the biomarkers LGALS9, CD7, IL2RG, PTPN7, ARHGEF6, CENTB1, SEPT6, SLA, LCP1, IFITM1, ZAP70, CXCR4, TM4SF2, ZNF91, ARHGDIB, TFDP2, ADA, CD99, CD3E, CD1C, STMN1, CD53, CD7, GNA15, CCND3, MAZ, SATB1, ZNF22, AES, AIF1, MYB, LCK, C5orf13, NKX2-5, ZNFN1A1, STAT5A, CHI3L2, LAPTM5, MAP4K1, DDX1 1, GPSM3, TRB, CD3D, CD3G, PRKCB1, CD1E, HCLS1, GATA3, TCF7, RHOG, CDW52, HMGB2, DGKA, ITGB2, PSMB9, IDH2, AES, MCM5, NUCB2, CORO1A, ARHGDIB, PTPRCAP, CD47, RHOH, LGALS9, ATP2A3, or a combination thereof, preferably biomarkers CD99, SRRM1, ARHGDIB, LAPTM5, VWF, ITM2A, ITGB2, LGALS9, INPP5D, SATB1, CD53, TFDP2, SLA, IL2RG, MFNG, CD37, GMFG, SELL, CDW52, LRMP, ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, PTPRCAP, GIT2, ZNFN1A1, CENTB1, LCP2, SPI1, GNA15, GZMA, CEP1, BLM, CD8A, SCAP1, CD2, CD1C, TNFRSF7, VAV1, MAP4K1, CCR7, C6or02, ALOX15B, BRDT, CD3G, PTPRC, LTB, ATP2A3, NVL, RASGRP2, LCP1, CORO1A, CXCR4, PRKD2, G AT A3, TRA@, PRKCB1, HEM1, KIAA0922, TARP, SEC31L2, PRKCQ, SH2D1A, CHRNA3, CD1A, LST1, LAIR1, CACNA1G, TRB@, SEPT6, HA-I, DOCK2, CD3D, TRD@, T3JAM, FNBP1, CD6, AIF1, FOLH1, CD1E, LY9, UGT2B17, ADA, CDKL5, TRIM, EVL, DATF1, RGC32, PRKCH, ARHGAP15, NOTCH1, BIN2, SEMA4G, DPEP2, CECR1, BCL1 IB, STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEF1, IL21R, MGC17330, AKAP13, ZNF335, GIMAP5, or a combination thereof, and most preferably biomarkers CD99, ARHGDIB, VWF, ITM2A, LGALS9, INPP5D, SATB1, TFDP2, SLA, IL2RG, MFNG, SELL, CDW52, LRMP, ICAM2, RIMS3, PTPN7, ARHGAP25, LCK, CXorf9, RHOH, GIT2, ZNFN1A1, CENTB1, LCP2, SPI1, GZMA, CEP1, CD8A, SCAP1, CD2, CD1C, TNFRSF7, VAV1, MAP4K1, CCR7, C6orf32, ALOX15B, BRDT, CD3G, LTB, ATP2A3, NVL, RASGRP2, LCP1, CXCR4, PRKD2, GATA3, TRA@, KIAA0922, TARP, SEC31L2, PRKCQ, SH2D1A, CHRNA3, CD1A, LST1, LAIR1, CACNA1G, TRB@, SEPT6, HA-I, DOCK2, CD3D, TRD@, T3JAM, FNBP1, CD6, AIF1, FOLH1, CD1E, LY9, ADA, CDKL5, TRIM, EVL, DATF1, RGC32, PRKCH, ARHGAP15, NOTCH1, BIN2, SEMA4G, DPEP2, CECR1, BCL1 IB, STAG3, GALNT6, UBASH3A, PHEMX, FLJ13373, LEF1, IL21R, MGC1 7330, AKAP13, ZNF335, GIMAP5, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Methylprednisolone.

In one embodiment, one or more of the biomarkers RPLP2, RPL4, HMGA1, RPL27, IMPDH2, LAMR1, PTMA, ATP5B, NPM1, NCL, RPS25, RPL9, TRAP1, RPL21, LAMR1, REA, HNRPA1, LDHB, RPS2, NME1, PAICS, EEF1B2, RPS15A, RPL19, RPL6, ATP5G2, SNRPF, SNRPG, RPS7, or a combination thereof, preferably biomarkers PRPF8, RPL 18, RNPS1, RPL32, EEF1G, GOT2, RPL13A, PTMA, RPS15, RPLP2, CSDA, KHDRBS1, SNRPA, IMPDH2, RPS19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRIM1, PFDN5, OXA1L, H3F3A, ATIC, RPL13, CIAPIN1, FBL, RPS2, PCCB, RBMX, SHMT2, RPLPO, HNRPA1, STOML2, RPS9, SKB1, GLTSCR2, CCNB11P1, MRPS2, FLJ20859, FLJ 12270, or a combination thereof, and most preferably biomarkers PRPF8, RPL18, GOT2, RPL13 A, RPS15, RPLP2, CSDA, KHDRBS1, SNRPA, IMPDH2, RPS19, NUP88, ATP5D, PCBP2, ZNF593, HSU79274, PRIM1, PFDN5, OXA1L, H3F3A, ATIC, CIAPIN1, RPS2, PCCB, SHMT2, RPLPO, HNRPA1, STOML2, SKB1, GLTSCR2, CCNB11P1, MRPS2, FLJ20859, FLJ 12270, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Methotrexate.

In one embodiment, one or more of the biomarkers ACTB, COL5A1, MT1E, CSDA, COL4A2, MMP2, COL1A1, TNFRSF1A, CFHL1, TGFBI, FSCN1, NNMT, PLAUR, CSPG2, NFIL3, C5orf13, NCOR2, TUBB4, MYLK, TUB A3, PLAU, COL4A2, COL6A2, COL6A3, IFITM2, PSMB9, CSDA, COL1A1, or a combination thereof, preferably biomarkers MSN, PFN1, HK1, ACTR2, MCL1, ZYX, RAP1B, GNB2, EPAS1, PGAM1, CKAP4, DUSP1, MYL9, K-ALPHA-I, LGALS1, CSDA, AKR1B1, IFITM2, ITGA5, VIM, DPYSL3, JUNB, ITGA3, NFKBIA, LAMB1, FHL1, INSIG1, TIMP1, GJA1, PSME2, PRG1, EXT1, DKFZP434J154, OPTN, M6PRBP1, MVP, VASP, ARL7, NNMT, TAP1, COL1A1, BASP1, PLOD2, ATF3, PALM2-AKAP2, IL8, ANPEP, LOXL2, TGFB1, IL4R, DGKA, STC2, SEC61G, NFIL3, RGS3, NK4, F2R, TPM2, PSMB9, LOX, STC1, CSPG2, PTGER4, IL6, SMAD3, PLAU, WNT5A, BDNF, TNFRSF1A, FLNC, DKFZP564K0822, FLOT1, PTRF, HLA-B, COL6A2, MGC4083, TNFRSF10B, PLAGL1, PNM A2, TFPI, LAT, GZMB, CYR61, PLAUR, FSCN1, ERP70, AF1Q, UBC, FGFR1, HIC, BAX, COL4A2, COL6A1, IFITM3, MAP1B, FLJ46603, RAFTLIN, RRAS, FTL, K1AA0877, MT1E, CDC10, DOCK2, TRIM22, RIS1, BCAT1, PRF1, DBN1, MT1K, TMSB10, RAB31, FLJ10350, C1orf24, NME7, TMEM22, TPK1, COL5A2, ELK3, CYLD, ADAMTS1, EHD2, ACTB, or a combination thereof, and most preferably biomarkers PFN1, HK1, MCL1, ZYX, RAP1B, GNB2, EPAS1, PGAM1, CKAP4, DUSP1, MYL9, K-ALPHA-1, LGALS1, CSDA, IFITM2, ITGA5, DPYSL3, JUNB, NFKBIA, LAMB1, FHL1, INSIG1, TIMP1, GJA1, PSME2, PRG1, EXT1, DKFZP434J154, MVP, VASP, ARL7, NNMT, TAP1, PLOD2, ATF3, PALM2-AKAP2, IL8, LOXL2, IL4R, DGKA, STC2, SEC61G, RGS3, F2R, TPM2, PSMB9, LOX, STC1, PTGER4, IL6, SMAD3, WNT5A, BDNF, TNFRSF1A, FLNC, DKFZP564K0822, FLOT1, PTRF, HLA-B, MGC4083, TNFRSF10B, PLAGL1, PNMA2, TFPI, LAT, GZMB, CYR61, PLAUR, FSCN1, ERP70, AF1Q, HIC, COL6A1, IFITM3, MAP1B, FLJ46603, RAFTLIN, RRAS, FTL, K1AA0877, MT1E, CDC10, DOCK2, TRIM22, RIS1, BCAT1, PRF1, DBN1, MT1K, TMSB10, FLJ10350, C1orf24, NME7, TMEM22, TPK1, COL5A2, ELK3, CYLD, ADAMTS1, EHD2, ACTB, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Bleomycin.

In one embodiment, one or more of the biomarkers NOS2A, MUC1, TFF3, GP1BB, IGLL1, BATF, MYB, PTPRS, NEFL, AIP, CEL, DGKA, RUNX1, ACTR1A, CLCNKA, or a combination thereof, preferably biomarkers PTMA, SSRP1, NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINA1, SSSCA1, EZH2, MYB, PRIM1, H2AFX, HMGA1, HMMR, TK2, WHSC1, DIAPH1, LAMB3, DPAGT1, UCK2, SERPINB1, MDN1, BRRN1, GOS2, RAC2, MGC21654, GTSE1, TACC3, PLEK2, PLAC8, HNRPD, PNAS-4, or a combination thereof, and most preferably biomarkers SSRP1, NUDC, CTSC, AP1G2, PSME2, LBR, EFNB2, SERPINA1, SSSCA1, EZH2, MYB, PRIM1, H2AFX, HMGA1, HMMR, TK2, WHSC1, DIAPH1, LAMB3, DPAGT1, UCK2, SERPINB1, MDN1, BRRN1, GOS2, RAC2, MGC21654, GTSE1, TACC3, PLEK2, PLAC8, HNRPD, PNAS-4, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Methyl-GAG.

In one embodiment, one or more of the biomarkers MSN, ITGA5, VIM, TNFAIP3, CSPG2, WNT5A, FOXF2, LOC94105, IFI1 6, LRRN3, FGFR1, DOCK10, LEPRE1, COL5A2, ADAMTS1, or a combination thereof, and most preferably biomarkers ITGA5, TNFAIP3, WNT5A, FOXF2, LOC94105, IFI16, LRRN3, DOCK10, LEPRE1, COL5A2, ADAMTS1, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to carboplatin.

In one embodiment, one or more of the biomarkers RPL1 8, RPL1OA, RNPS1, ANAPC5, EEF1B2, RPL13A, RPS15, AKAP1, NDUFAB1, APRT, ZNF593, MRP63, IL6R, RPL13, SART3, RPS6, UCK2, RPL3, RPL1 7, RPS2, PCCB, TOMM20, SHMT2, RPLPO, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, CALML4, or a combination thereof, and most preferably biomarkers RPL18, RPL1OA, ANAPC5, EEF1B2, RPL13A, RPS15, AKAP1, NDUFAB1, APRT, ZNF593, MRP63, IL6R, SART3, UCK2, RPL17, RPS2, PCCB, TOMM20, SHMT2, RPLPO, GTF3A, STOML2, DKFZp564J157, MRPS2, ALG5, CALML4, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to 5-FU(5-Fluorouracil).

In one embodiment, one or more of the biomarkers ITK, KIFC 1, VLDLR, RUNX 1, PAFAH 1B3, H1FX, RNF 144, TMSNB, CRY1, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADD1, RFC5, TM4SF2, PFN2, BMI1, TUBGCP3, ATP6V1B2, RALY, PSMC5, CD1D, ADA, CD99, CD2, CNP, ERG, MYL6, CD3E, CD1A, CD1B, STMN1, PSMC3, RPS4Y1, AKT1, TALI, GNA15, UBE2A, TCF12, UBE2S, CCND3, PAX6, MDK, CAPG, RAG2, ACTN1, GSTM2, SATB1, NASP, IGFBP2, CDH2, CRABP1, DBN1, CTNNA1, AKR1C1, CACNB3, FARSLA, CASP2, CASP2, E2F4, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, CPSF1, GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHL1, EZH2, SMOX, SLC4A2, UFD1L, SEPW1, ZNF32, HTATSF1, SHD1, PTOV1, NXF1, FYB, TRIM28, BC008967, TRB@, TFRC, H1FO, CD3D, CD3G, CENPB, ALDH2, ANXA1, H2AFX, CD1E, DDX5, ABL1, CCNA2, ENO2, SNRPB, GATA3, RRM2, GLUL, TCF7, FGFR1, SOX4, MAL, NUCB2, SMA3, FAT, UNG, ARHGDIB, RUNX1, MPHOSPH6, DCTN1, SH3GL3, VIM, PLEKHC1, CD47, POLR2F, RHOH, ADD1, ATP2A3, or a combination thereof, preferably biomarkers ITK, KIFC1, VLDLR, RUNX1, PAF AH1 B3, H1FX, RNF 144, TMSNB, CRY1, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADD1, RFC5, TM4SF2, PFN2, BMI1, TUBGCP3, ATP6V1B2, RALY, PSMC5, CD1D, ADA, CD99, CD2, CNP, ERG, MYL6, CD3E, CD1A, CD1B, STMN1, PSMC3, RPS4Y1, AKT1, TALI, GNA15, UBE2A, TCF12, UBE2S, CCND3, PAX6, MDK, CAPG, RAG2, ACTN1, GSTM2, SATB1, NASP, IGFBP2, CDH2, CRABP1, DBN1, CTNNA1, AKR1C1, CACNB3, FARSLA, CASP2, CASP2, E2F4, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, CPSF1, GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHL1, EZH2, SMOX, SLC4A2, UFD1L, SEPW1, ZNF32, HTATSF1, SHD1, PTOV1, NXF1, FYB, TRIM28, BC008967, TRB@, TFRC, H1FO, CD3D, CD3G, CENPB, ALDH2, ANXA1, H2AFX, CD1E, DDX5, ABL1, CCNA2, ENO2, SNRPB, GATA3, RRM2, GLUL, TCF7, FGFR1, SOX4, MAL, NUCB2, SMA3, FAT, UNG, ARHGDIB, RUNX1, MPHOSPH6, DCTN1, SH3GL3, VIM, PLEKHC1, CD47, POLR2F, RHOH, ADD1, ATP2A3, or a combination thereof, and most preferably biomarkers KIFC1, VLDLR, RUNX1, PAFAH 1 B3, H1FX, RNF 144, TMSNB, CRY1, MAZ, SLA, SRF, UMPS, CD3Z, PRKCQ, HNRPM, ZAP70, ADD1, RFC5, TM4SF2, PFN2, BMI1, TUBGCP3, ATP6V1B2, CD1D, ADA, CD99, CD2, CNP, ERG, CD3E, CD1A, PSMC3, RPS4Y1, AKT1, TALI, UBE2A, TCF12, UBE2S, CCND3, PAX6, RAG2, GSTM2, SATB1, NASP, IGFBP2, CDH2, CRABP1, DBN1, AKR1C1, CACNB3, CASP2, CASP2, LCP2, CASP6, MYB, SFRS6, GLRB, NDN, GNAQ, TUSC3, GNAQ, JARID2, OCRL, FHL1, EZH2, SMOX, SLC4A2, UFD1L, ZNF32, HTATSF1, SHD1, PTOV1, NXF1, FYB, TRIM28, BC008967, TRB@, H1FO, CD3D, CD3G, CENPB, ALDH2, ANXA1, H2AFX, CD1E, DDX5, CCNA2, ENO2, SNRPB, GATA3, RRM2, GLUL, SOX4, MAL, UNG, ARHGDIB, RUNX1, MPHOSPH6, DCTN1, SH3GL3, PLEKHC1, CD47, POLR2F, RHOH, ADD1, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates chemosensitivity to Rituximab (e.g., MABTHERA™).

In one embodiment, one or more of the biomarkers CCL21, ANXA2, SCARB2, MAD2L1BP, CAST, PTS, NBL1, ANXA2, CD151, TRAM2, HLA-A, CRIP2, UGCG, PRSS1 1, MME, CBR1, LGALS1, DUSP3, PFN2, MICA, FTH1, RHOC, ZAP 128, P0N2, COL5A2, CST3, MCAM, IGFBP3, MMP2, GALIG, CTSD, ALDH3A1, CSRP1, S100A4, CALD1, CTGF, CAPG, HLA-A, ACTN1, TAGLN, FSTL1, SCTR, BLVRA, COPEB, DIPA, SMARCD3, FN1, CTSL, CD63, DUSP1, CKAP4, MVP, PEA15, S100A13, ECE1, or a combination thereof, preferably biomarkers TRA1, ACTN4, WARS, CALM1, CD63, CD81, FKBP1A, CALU, IQGAP1, CTSB, MGC8721, STAT1, TACC1, TM4SF8, CD59, CKAP4, DUSP1, RCN1, MGC8902, LGALS1, BHLHB2, RRBP1, PKM2, PRNP, PPP2CB, CNN3, ANXA2, IER3, JAK1, MARCKS, LUM, FER1L3, SLC20A1, EIF4G3, HEXB, EXT1, TJP1, CTSL, SLC39A6, RI0K3, CRK, NNMT, COL1A1, TRAM2, ADAM9, DNAJC7, PLSCR1, PRSS23, PLOD2, NPC1, TOB1, GFPT1, IL8, DYRK2, PYGL, LOXL2, KIAA0355, UGDH, NFIL3, PURA, ULK2, CENTG2, NID2, CAP350, CXCL1, BTN3A3, IL6, WNT5A, FOXF2, LPHN2, CDH1 1, P4HA1, GRP58, ACTN1, CAPN2, DSIPI, MAP1LC3B, GALIG, IGSF4, IRS2, ATP2A2, OGT, TNFRSF10B, K1AA1 128, TM4SF1, RBPMS, RIPK2, CBLB, NR1D2, BTN3A2, SLC7A1 1, MPZL1, IGFBP3, SS A2, FN15 NQO1, ASPH, ASAH1, MGLL, SERPINB6, HSPA5, ZFP36L1, COL4A2, COL4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAP1B, MAPK1, MYO1B, CAST, RRAS2, QKI, LHFPL2, 38970, ARHE, KIAA1078, FTL, KIAA0877, PLCB1, KIAA0802, KPNB1, RAB3GAP, SERPINB1, TIMM 17A, SOD2, HLA-A, NOMO2, LOC55831, PHLDA1, TMEM2, MLPH, FAD104, LRRC5, RAB7L1, FLJ35036, DOCK10, LRP12, TXNDC5, CDC14B, HRMT1L1, CORO1C, DNAJC10, TNPO1, LONP, AMIGO2, DNAPTP6, ADAMTS1, or a combination thereof, and most preferably biomarkers TRA1, ACTN4, CALM1, CD63, FKBP1A, CALU, IQGAP1, MGC8721, STAT1, TACC1, TM4SF8, CD59, CKAP4, DUSP1, RCN1, MGC8902, LGALS1, BHLHB2, RRBP1, PRNP, IER3, MARCKS, LUM, FER1L3, SLC20A1, HEXB, EXT1, TJP1, CTSL, SLC39A6, RIOK3, CRK, NNMT, TRAM2, ADAM9, DNAJC7, PLSCR1, PRSS23, PLOD2, NPC1, TOB1, GFPT1, IL8, PYGL, LOXL2, K1AA0355, UGDH, PURA, ULK2, CENTG2, NID2, CAP350, CXCL1, BTN3A3, IL6, WNT5A, FOXF2, LPHN2, CDHI 1, P4HA1, GRP58, DSIPI, MAP1LC3B, GALIG, IGSF4, IRS2, ATP2A2, OGT, TNFRSF10B, K1AA1 128, TM4SF1, RBPMS, RIPK2, CBLB, NR1D2, SLC7A1 1, MPZL1, SSA2, NQO1, ASPH, ASAH1, MGLL, SERPINB6, HSPA5, ZFP36L1, COL4A1, CD44, SLC39A14, NIPA2, FKBP9, IL6ST, DKFZP564G2022, PPAP2B, MAP1B, MAPK1, MYO1B, CAST, RRAS2, QKI, LHFPL2, 38970, ARHE, KIAA1078, FTL, KIAA0877, PLCB1, KIAA0802, RAB3GAP, SERPINB1, TIMM17A, S0D2, HLA-A, NOMO2, LOC55831, PHLDA1, TMEM2, MLPH, FAD104, LRRC5, RAB7L1, FLJ35036, DOCK10, LRP12, TXNDC5, CDCl₄B, HRMT1L1, CORO1C, DNAJC10, TNPO1, LONP, AMIG02, DNAPTP6, ADAMTS1, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates sensitivity to radiation therapy.

In one embodiment, one or more of the biomarkers FAU, NOL5 A, ANP32A, ARHGDIB, LBR, FABP5, ITM2A, SFRS5, IQGAP2, SLC7A6, SLA, IL2RG, MFNG, GPSM3, PIM2, EVER1, LRMP, ICAM2, RIMS3, FMNL1, MYB, PTPN7, LCK, CXorf9, RHOH, ZNFN1A1, CENTB1, LCP2, DBT, CEP1, IL6R, VAV1, MAP4K1, CD28, PTP4A3, CD3G, LTB, USP34, NVL, CD8B1, SFRS6, LCP15 CXCR4, PSCDBP, SELPLG, CD3Z, PRKCQ, CD1A, GATA2, P2RX5, LAIR1, C1orf38, SH2D1A, TRB@, SEPT6, HA-I, DOCK2, WBSCR20C, CD3D, RNASE6, SFRS7, WBSCR20A, NUP210, CD6, HNRPA1, AIF1, CYFIP2, GLTSCR2, C1 1orf2, ARHGAP15, BIN2, SH3TC1, STAG3, TM6SF1, C15orf25, FLJ22457, PACAP, MGC2744, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates sensitivity to an HDAC inhibitor.

In one embodiment, one or more of the biomarkers CD99, SNRPA, CUGBP2, STAT5A, SLA, IL2RG, GTSE1, MYB, PTPN7, CXorf9, RHOH, ZNFN1A1, CENTB1, LCP2, HIST1H4C, CCR7, APOBEC3B, MCM7, LCP1, SELPLG, CD3Z, PRKCQ, GZMB, SCN3A, LAIR1, SH2D1A, SEPT6, CG018, CD3D, C18orf1O, PRF1, AIF1, MCM5, LPXN, C22orf18, ARHGAP15, LEF1, or a combination thereof, is assessed of a membrane vesicle, and expression of the one or more biomarkers indicates sensitivity to 5-Aza-2′-deoxycytidine (Decitabine).

Examples of therapeutics that can be assessed by methods of the invention include the therapeutics disclosed in the following U.S. patent application (referenced by publication number): US20090246199; US20110117079; US20100196385; US20070231325, US20100221212, US20100303812, US20090203639, US20090034308, US20070213266, US20110165162, US20100119526, US20100129356, US20080014196, US201000316637, US20080187532, US20080175847; or International Patent Publ. No. WO/2010/032060, entitled “ANTIBODIES DIRECTED TO DLL4 AND USES THEREOF”; each of which disclosure is incorporated herein by reference in its entirety. For example, a biological sample from target subject is assayed to determine a biosignature comprising one or more biomarkers disclosed herein, where the target subject(s) is administered or may be administered one or more therapeutics. In such an example, the biosignature when compared to a reference sample (which may be from a different individual or the same test subject) provides a readout that indicates to the clinician that the test subject is responding/not responding or is likely to respond/not respond to the particular therapeutic. The therapeutic treatment can also be an agent that associates with DLL4, either directly or indirectly. For example, the therapeutic can be an agent that blocks DLL4's role in tumor angiogenesis. In some embodiments, the therapeutic treatment comprises an anti-DLL4 antibody or fragment thereof, an anti-DLL4 antibody drug-conjugate, a cancer vaccine directed to DLL4, a peptide or nucleic acid that binds DLL4, a soluble fragment of DLL4, or an anti-VEGF therapy such as bevacizumab. The therapeutic can be an agent that disrupts the DLL4/Notch signaling pathway and/or VEGF pathways. See, e.g., Li et al., Cancer Res. 2007 Dec. 1; 67(23):11244-53, which publication is herein incorporated by reference in its entirety.

Treatment selection based on detecting of one or more biomarkers is also disclosed in PCT Publication No. WO2008138578, which is herein incorporated by reference in its entirety.

Cardiovascular

Assessing a vesicle can be used in the theranosis of a cardiovascular condition, disorder, or disease. A cardiovascular condition includes, but is not limited to, chronic rheumatic heart disease, hypertensive disease, ischemic heart disease, pulmonary circulatory disease, heart disease, cerebrovascular disease, diseases of arteries, arterioles and capillaries and diseases of veins and lymphatics. A chronic rheumatic heart disease includes, but is not limited to diseases of mitral valve, diseases of aortic valve, diseases of mitral and aortic valves, and diseases of other endocardial structures. A hypertensive disease includes, but is not limited to essential hypertension, hypertension, malignant, hypertension, benign, hypertension, unspecified, hypertensive heart disease, hypertensive renal disease, hypertensive renal disease, unspecified, with renal failure, hypertensive heart and renal disease, hypertension, renovascular, malignant, and hypertension, renovascular benign. An ischemic heart disease includes, but is not limited to acute myocardial infarction, myocardiac infarction, acute, anterolateral, myocardiac infarction, acute, anterior, myocardiac infarction, acute, inferolateral, myocardiac infarction, acute, inferoposterior, myocardiac infarction, acute, other inferior wall, myocardiac infarction, acute, other lateral wall, myocardiac infarction, acute, true posterior, myocardiac infarction, acute, subendocardial, myocardiac infarction, acute, spec, myocardiac infarction, acute, unspecified, postmyocardial infarction syndrome, intermediate coronary syndrome, old myocardial infarction, angina pectoris, angina decubitus, prinzmetal angina, coronary atherosclerosis, aneurysm and dissection of heart, aneurysm of heart wall, aneurysm of coronary vessels, dissection of coronary artery, and unspecified chronic ischemic heart disease.

A pulmonary circulatory disease includes, but is not limited to, diseases of pulmonary circulation, acute pulmonary heart disease, pulmonary embolism, not iatrogenic, chronic pulmonary heart disease, and unspecified chronic pulmonary heart disease. A heart disease includes, but is not limited to acute pericarditis, other and unspecified acute pericarditis, acute nonspecific pericarditis, acute and subacute endocarditis, acute bacterial endocarditis acute myocarditis, other and unspecified acute myocarditis, myocarditis, idiopathic, other diseases of pericardium, other diseases of endocardium, alvular disorder, mitral, valvular disorder, aortic, valvular disorder, tricuspid, valvular disorder, pulmonic, cardiomyopathy, hypertrophic obstructive cardiomyopathy, conduction disorders, atrioventricular block, third degree, atrioventricular block, first degree, atrioventricular block, mobitz ii, atrioventricular block, wenckebach's, bundle branch block, left, bundle branch block, right, sinoatrial heart block, atrioventricular excitation, anomalous, Wolff Parkinson White syndrome, cardiac dysrhythmias, tachycardia, paroxysmal supraventricular, atrial fibrillation and flutter, atrial fibrillation, atrial flutter, ventricular fibrillation and flutter, ventricular fibrillation, cardiac arrest, premature beats, other specified cardiac dysrhythmias, sick sinus syndrome, sinus bradycardia, cardiac dysrhythmia unspecified, gallop rhythm, heart failure, heart failure, congestive, acute pulmonary edema, systolic unspecified heart failure, acute systolic heart failure, chronic systolic heart failure, diastolic unspecified heart failure, diastolic chronic heart failure, combined unspecified heart failure, and cardiomegaly.

A cerebrovascular disease includes, but is not limited to subarachnoid hemorrhage, intracerebral hemorrhage, other and unspecified intracranial hemorrhage, intracranial hemorrhage, occlusion and stenosis of precerebral arteries, occlusion and stenosis of basilar artery, occlusion and stenosis of carotid artery, occlusion and stenosis of vertebral artery, occlusion of cerebral arteries, cerebral thrombosis, cerebral thrombosis without cerebral infarction, cerebral thrombosis with cerebral infarction, cerebral embolism, cerebral embolism without cerebral infarction, cerebral embolism with cerebral infarction, transient cerebral ischemia, basilar artery syndrome, vertebral artery syndrome, subclavian steal syndrome, vertebrobasilar artery syndrome, transient ischemic attack, acute but ill defined cerebrovascular disease, ill defined cerebrovascular disease, cerebral atherosclerosis, other generalized ischemic cerebrovascular disease, hypertensive encephalopathy, cerebral aneurysm nonruptured, cerebral arteritis, moyamoya disease, nonpyogenic thrombosis of intracranial venous sinus, transient global amnesia, late effects of cerebrovascular disease, cognitive deficits, speech and language deficits, unspecified speech and language deficits, aphasia, dysphasia, other speech and language deficits, hemiplegia/hemiparesis, hemiplegia affecting unspecified side, hemiplegia affecting dominant side, hemiplegia affecting nondominant side, monoplegia of upper limb, monoplegia of lower limb, other paralytic syndrome, other late effects of cerebrovascular disease, apraxia cerebrovascular disease, dysphagia cerebrovascular disease, facial weakness, ataxia, and vertigo.

Diseases of arteries, arterioles and capillaries include, but are not limited to atherosclerosis, atherosclerosis of renal artery, atherosclerosis of native arteries of the extremities, intermittent claudication, atherosclerosis, extremities, without ulceration, atherosclerosis, not heart/brain, aortic aneurysm, dissection of aorta, abdominal ruptured aortic aneurysm, abdominal, without ruptured aortic aneurysm, unspecified aortic aneurysm, other aneurysm, other peripheral vascular disease, raynaud's syndrome, thromboangiitis obliterans, other arterial dissection, dissection of carotid artery, dissection of iliac artery, dissection of renal artery, dissection of vertebral artery, dissection of other artery, erythromelalgia, unspecified peripheral vascular disease, arterial embolism and thrombosis, polyarteritis nodosa and allied conditions, polyarteritis nodosa, kawasaki disease/acute febrile mucocutaneous lymph node syndrome, hypersensitivity angiitis, goodpasture's syndrome, lethal midline granuloma, wegener's granulomatosis, giant cell arteritis, thrombotic microangiopathy, takayasu's disease, other disorders of arteries and arterioles, arteriovenous fistula acquired, arteritis unspecified, vasculitis, and vascular non-neoplastic nevus.

Diseases of veins and lymphatics include, but are not limited to, phlebitis and thrombophlebitis, femoral deep vein thrombosis, deep vein thrombosis of other leg veins, phlebitis of other sites, superficial veins of upper extremity, unspecified thrombophlebitis, portal vein thrombosis, other venous embolism and thrombosis, unspecified deep vein thrombosis, proximal deep vein thrombosis, distal deep vein thrombosis, unspecified venous embolism, varicose veins of lower extremities, varicose veins without ulcer, varicose veins without inflammation, varicose veins withoutulcer, inflammation, varicose veins, asymptomatic, hemorrhoids, hemorrhoids, internal without complication, hemorrhoids, external without complication, hemorrhoids, external thrombosed, hemorrhoids, varicose veins of other sites, esophageal varices without bleeding, esophageal varices without bleeding, varicocele, noninfective disorders of lymphatic channels, postmastectomy lymphedema syndrome, hypotension, orthostatic hypotension, iatrogenic hypotension, other disorders of circulatory system, other specified disorders of circulatory system, and unspecified venous insufficiency.

Other examples of cardiac conditions include, without limitation, coronary artery occlusion (e.g., resulting from or associated with lipid/cholesterol deposition, macrophage/inflammatory cell recruitment, plaque rupture, thrombosis, platelet deposition, or neointimal proliferation); ischemic syndromes (e.g., resulting from or associated with myocardial infarction, stable angina, unstable angina, coronary artery restenosis or reperfusion injury); cardiomyopathy (e.g., resulting from or associated with an ischemic syndrome, a cardiotoxin, an infection, hypertension, a metabolic disease (such as uremia, beriberi, or glycogen storage disease), radiation, a neuromuscular disease, an infiltrative disease (such as sarcoidosis, hemochromatosis, amyloidosis, Fabry's disease, or Hurler's syndrome), trauma, or an idiopathic cause); arrhythmia or dysrrhythmia (e.g., resulting from or associated with an ischemic syndrome, a cardiotoxin, adriamycin, an infection, hypertension, a metabolic disease, radiation, a neuromuscular disease, an infiltrative disease, trauma, or an idiopathic cause); infection (e.g., caused by a pathogenic agent such as a bacterium, a virus, a fungus, or a parasite); and an inflammatory condition (e.g., associated with myocarditis, pericarditis, endocarditis, immune cardiac rejection, or an inflammatory conditions resulting from one of idiopathic, autoimmune, or a connective tissue disease).

Cardiovascular: Biosignature

A biosignature of a vesicle can be assessed to provide a theranosis for a subject. The biosignature of the vesicle can comprise one or more biomarkers such as, but not limited to, any one or more biomarkers as described herein, such as, but not limited to, those listed in FIG. 24, miR-21, miR-129, miR-212, miR-214, miR-134, and others such as described in US Publication No. 2010/0010073.

Cardiovascular: Standard of Care

Determining the biosignature of a vesicle, the amount of vesicles, or both, of a sample from a subject suffering from a cardiac condition, disorder, or disease, can be used select a standard of care for the subject. The standard of care may include therapeutic agents or procedures (e.g., angioplasty). Examples of therapeutic agents include, without limitation, angiogenesis promoters (e.g., vascular endothelial growth factor, nitric oxide releasing or generating agents, fibroblast growth factor, platelet derived growth factor, interleukin-6, monocyte chemotactic protein-1, granulocyte-macrophage colony stimulating factor, transforming growth factor-.beta.), anti-thrombotic agents (e.g., aspirin, heparin, PPACK, enoxaprin, hirudin), anticoagulants, antibiotics, antiplatelet agents, thrombolytics (e.g., tissue plasminogen activator), antiproliferatives, antiinflammatories, agents that inhibit hyperplasia, agents that inhibit restenosis, smooth muscle cell inhibitors, growth factors, growth factor inhibitors, cell adhesion inhibitors, chemotherapeutic agents, and combinations thereof.

For example, detection of one or more microRNAs biomarkers, such as miR-21, miR-129, miR-212, miR-214, miR-134 or a combination thereof from vesicles can be used to characterize a cardiac hypertrophy and/or heart failure, which provides a theranosis for the cardiac hypertrophy. The theranosis can include selecting a therapy such as adminstering angiogenesis promoters. Other examples of treatments include those for treating abnormal cholesterol and/or triglyceride levels in the blood, such as listed in Table 15.

TABLE 15 Examples of Classes of Drugs for Treatment of Cardiovascular Conditions Class Mechanism of Action Examples Statins Competitive inhibitors of HMG-CoA reductase Atorvastatin, Simvastatin, Pravastatin, Fluvastatin, Rosuvastatin, Lovastatin, Pitavastatin, Cerivastatin (withdrawn) Fibrates PPARα activators Fenofibrate, Bezafibrate, Gemfibrozil, clofibrate, ciprofibrate Cholesterol May inhibit NCP1L1 in gut Ezetimibe Absorption Inhibitors Nicotinic Inhibits cholesterol and triglyceride synthesis, exact Niacin Acid mechanism unknown Derivatives Bile Acid Interrupt the enterohepatic circulation of bile acids Colesevelam, Sequestrants Cholestyramine, Colestimide, Colestipol Cholesteryl Inhibit cholesteryl ester transfer protein, a plasma protein that JTT-705, CETi-1, Ester Transfer mediates the exchange of cholesteryl esters from antiatherogenic Torcetrapib Protein HDL to proatherogenic apoliprotein B-containing lipoproteins Inhibitors Reverse Lipid Stimulate reverse lipid transport, a four-step process form ETC-216, ETC-588, ETC- Transport removing excess cholesterol and other lipids from the walls of 642, ETC-1001, ESP-1552, Pathway arteries and other tissues ESP-24232 Activators Antioxidants/ Inhibit vascular inflammation and reduce cholesterol levels; AGI-1067, Probucol Vascular block oxidant signals that switch on vascular cellular adhesion (withdrawn) Protectants molecule (VCAM)-1 Acyl-CoA Inhibit ACAT, which catalyzes cholesterol esterification, Eflucimibe, Pactimibe, Cholesterol regulates intracellular free cholesterol, and promotes cholesterol Avasimibe (withdrawn), Acyltransferase absorption and assemble of VLDL SMP-797 (ACAT) Inhibitors Peroxisome Activate PPARs, e.g., PPARα, γ, and possibly δ, which have a Tesaglitazar, GW-50516, Proliferator variety of gene regulatory functions GW-590735, LY-929, LY- Activated 518674, LY-465608, LY- Receptor 818 Agonists Microsomal Inhibit MTTP, which catalyze the transport of triglycerides, Implitapide, CP-346086 Triglyceride cholesteryl ester, and phosphatidylcholine between membranes; Transfer required for the synthesis of ApoB. Protein (MTTP) Inhibitors Squalene Interfere with cholesterol synthesis by halting the action of liver TAK-475, ER-119884 Synthase enzymes; may also slow or stop the proliferation of several cell Inhibitors types that contribute to atherosclerotic plaque formation Lipoprotein Directly activate lipoprotein lipase, which promotes the Ibrolipim (NO-1886) Lipase breakdown of the fat portion of lipoproteins Activators Liproprotein(a) Not yet established Gembacene Antagonists Bile Acid Inhibit intestinal epithelial uptake of bile acids. AZD-7806, BARI-1453, S- Reabsorption 8921 Inhibitors

In one embodiment, a treatment can be selected for a subject suffering from Peripheral Arterial Disease. One or more biomarkers, such as, but not limited to, C-reactive protein (CRP), serum Amylyoid A (SAA), interleukin-6, intracellular adhesion molecule (ICAM), vascular adhesion molecule (VCAM), CD40L, fibrinogen, fibrin D-dimer, fibrinopeptide A, von Willibrand factor, tissue plasminogen activator antigen (t-PA), factor VII, prothrombin fragment 1, oxidized low density lipoprotein (oxLDL), and lipoprotein A, can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, Atorvastatin, Simvastatin, Rosuvastatin, Pravastatin, Fluvastatin, Lovastatin, or a combination thereof.

In another embodiment, a treatment can be selected for a subject suffering from an arrhythmia. One or more biomarkers, such as, but not limited to, SERCA, AAP, Connexin 40, Connexin 43, ATP-sensitive potassium channel, Kv1.5 channel, and acetylcholine-activated posassium channel, can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, Disopyramide, Flecamide, Lidocaine, Mexiletine, Moricizine, Procainamide, Propafenone, Quinidine, Tocamide, Acebutolol, Atenolol, Betaxolol, Bisoprolol, Carvedilol, Esmolol, Metoprolol, Nadolol, Propranolol, Sotalol, Timolol, Amiodarone, Azimilide, Bepridil, Dofetilide, Ibutilide, Tedisamil, Diltiazem, Verapamil, Azimilide, Dronedarone, Amiodarone, PM101, ATI-2042, Tedisamil, Nifekalant, Ambasilide, Ersentilide, Trecetilide, Almokalant, D-sotalol, BRL-32872, HMR1556, L768673, Vernakalant, AZD70009, AVE0118, 59947, NIP-141/142, XEN-D0101/2, Ranolazine, Pilsicamide, JTV519, Rotigaptide, GAP-134, or a combination thereof.

In another embodiment, a treatment can be selected for a subject suffering from abnormal coagulation. One or more biomarkers, such as, but not limited to, F1.2, TAT, FPA, beta-throboglobulin, platelet factor 4, soluble P-selectin, IL-6, and CRP can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, aspirin, anticoagulants, ximelagatran, Heparin, Warfarin, or a combination thereof.

In another embodiment, a treatment can be selected for a subject suffering from Premature Atherosclerosis. One or more biomarkers, such as, but not limited to, CRP, NF-kB, IL-1, IL-6, IL-18, Apo-B, Lp-PLA2, Fibrinogen, Hcy, and Hcy-thiolactone can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment.

In yet another embodiment, a treatment can be selected for a subject suffering from Hypertension. One or more biomarkers, such as, but not limited to, Brain natriuretic peptide and N-terminal prohormone BNP, can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment.

In another embodiment, a treatment can be selected for a subject suffering from Cardiovascular Disease. One or more biomarkers, such as, but not limited to, an ACE inhibitor or angiotensin can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, lisinopril, candesartan, enalapril, or a combination thereof.

Thus, a treatment can be selected for the subject suffering from a cardiology related condition or cardiovascular condition, based on the biosignature of the subject's vesicle.

Autoimmune

Assessing a vesicle can be used in the theranosis of an autoimmune condition, disorder, or disease. Autoimmune conditions are conditions where a mammal's immune system starts reacting against its own tissues. Such conditions include, without limitation, systemic lupus erythematosus (SLE), discoid lupus, lupus nephritis, sarcoidosis, inflammatory arthritis, including juvenile arthritis, rheumatoid arthritis, psoriatic arthritis, Reiter's syndrome, ankylosing spondylitis, and gouty arthritis, multiple sclerosis, hyper IgE syndrome, polyarteritis nodosa, primary biliary cirrhosis, inflammatory bowel disease, Crohn's disease, celiac's disease (gluten-sensitive enteropathy), autoimmune hepatitis, pernicious anemia, autoimmune hemolytic anemia, psoriasis, scleroderma, myasthenia gravis, autoimmune thrombocytopenic purpura, autoimmune thyroiditis, Grave's disease, Hasimoto's thyroiditis, immune complex disease, chronic fatigue immune dysfunction syndrome (CFIDS), polymyositis and dermatomyositis, cryoglobulinemia, thrombolysis, cardiomyopathy, pemphigus vulgaris, pulmonary interstitial fibrosis, asthma, Churg-Strauss syndrome (allergic granulomatosis), atopic dermatitis, allergic and irritant contact dermatitis, urtecaria, IgE-mediated allergy, atherosclerosis, vasculitis, idiopathic inflammatory myopathies, hemolytic disease, Alzheimer's disease, chronic inflammatory demyelinating polyneuropathy, chagas disease, chronic obstruct pulmonary disease, dermatomyositis, diabetes mellitus type 1, endometriosis, goodpasture's syndrome, graves' disease, guillain-barre syndrome (gbs), Hashimoto's disease, hidradenitis suppurat a, kawasaki disease, iga nephropathy, idiopathic thrombocytopenic purpura, interstitial cystitis, lupus erythematosus i, mixed connect e tissue disease, morphea, myasthenia gravis, narcolepsy, neuromyotonia, pemphigus vulgaris, pernicious anaemia, psoriasis, psoriatic arthritis, polymyositis, primary biliary cirrhosis, rheumatoid arthritis, schizophrenia, scleroderma, sjögren's syndrome, stiff person syndrome, temporal arteritis, ulcerat e colitis, vasculitis, vitiligo, Wegener's granulomatosis, and AID.

Autoimmune: Biosignature

A biosignature of a vesicle can be assessed to provide a theranosis for a subject. The biosignature of the vesicle can comprise one or more biomarkers such as, but not limited to, a biomarker such as listed in FIG. 1 for autoimmune disease, or for other autoimmune diseases, such as, but not limited to those listed in FIGS. 23, 34, 35, 36, 39, 41, 42, and 56.

Autoimmune: Standard of Care

Determining the biosignature of a vesicle, the amount of vesicles, or both, of a sample from a subject suffering from an autoimmune condition, disorder or disease can be used to select a standard of care for the subject. Most autoimmune diseases cannot yet be treated directly, but are treated according to symptoms associated with the condition. The standard of care includes, for example, prescribing corticosteroid drugs, non-steroidal anti-inflammatory drugs (NTHEs) or more powerful immunosuppressant drugs such as cyclophosphamide, methotrexate and azathioprine that suppress the immune response and stop the progression of the disease. Radiation of the lymph nodes and plasmapheresis (a procedure that removes the diseased cells and harmful molecules from the blood circulation) are other ways of treating an autoimmune disease.

Examples of drugs or agents for use in treating autoimmune diseases, which can be selected based on a profiling of a vesicle from a subject, include those in Table 16 for a subject suffering from diabetes, in Table 17 for those suffering from Multiple Sclerosis.

TABLE 16 Example of Classes of Drugs for Treatment of Diabetes Class Mechanism of Action Examples Peroxisome Target PPAR-gamma or PPAR-gamma and -alpha (see below). Rosiglitazone, Pioglitazone, Proliferator- PPAR are nuclear receptors that help regulate glucose and lipid Balaglitazone, see also Activated metabolism. Activation of PPAR-gamma improves insulin others described herein Receptor sensitivity and thus improves glycemic control. (PPAR) Agonists Dual-Action Act on both PPAR-gamma and PPAR-alpha. PPAR-alpha TAK-559, Muraglitazar, Peroxisome activation has effects on cellular uptake of fatty acids and their Tesaglitazar, Netoglitazone, Proliferator- oxidation, and on lipoprotein metabolism. May also act to reduce see also others described Activated inflammatory response in vascular endothelial cells. herein Receptor Agonists Biguanidines Complete mechanism is not known. Reduces gluconeogenesis in Metformin, Metformin GR the liver by inhibiting glucose-6-phosphatase. Sulfonylureas Induce insulin secretion by binding to cellular receptors that Glimepride, cause membrane depolarization and insulin exocytosis. Glyburide/glibenclamide, Glipizide, Gliclazide. Tobutamide Insulin and Supplements endogenous insulin. Insulin analogs have a variety Insulin lispro, Insulin aspart, Insulin of amino acid changes and have altered onset of action and Insulin glargine, Exubera, Analogs duration of action, as well as other properties, compared to native AERx Insulin Diabetes (Injectable, insulin. Inhaled insulin is absorbed through the alveoli. Spray Management System, HIM- Inhaled, Oral, oral insulin is absorbed by the buccal mucosa and intranasal 2, Oaralin, Insulin detemir, Transdermal, through the nasal mucosa. Transdermal insulin is absorbed Insulin glulisine Intranasal) through the skin. Meglitinides Are thought to bind to a nonsulfonylurea beta cell receptor and Repaglinide, Nateglinide, act to cause insulin secretion by mechanism similar to Mitiglinide sulfonylureas Alpha- Inhibit carbohydrate digestion. Act at brush border of intestinal Acarbose, Miglitol, Glucosidase epithelium. Voglibose Inhibitors Glucagon- Diabetic patients may lack native GLP-1, and anlalogs act as Exenatide, Exenatide LAR, Like substitutes. GLP-1 is an intestinal peptide hormone that induces Liraglutide, ZP 10, Peptide(GLP)- glucose-dependent insulin secretion, controls gastric emptying, BN51077, 1 Analogs inhibits appetite, and modulates secretion of glucagon and somatostatin. Dipeptidyl Inhibit DPP-IV, a ubiquitous enzyme that cleaves and inactivates LAF-237, p-32/98, MK- Peptidase GLP-1, thus inhibition of DPP-IV increases GLP-1 activity 431, P3298, NVP LAF 237, (DPP)-IV Inhibitors Pancreatic Inhibits lipases, thus inhibiting uptake of dietary fat. This causes Orlistat Lipase weight loss, improves insulin sensitivity and lowers Inhibitors hyperglycemia. Amylin Act to augment amylin, which acts with insulin by slowing Pramlintide Analogs glucose absorption from the gut and slows after-meal glucose release from liver. Dopamine Thought to act to alleviate abnormal daily variations in central Bromocriptine D2 receptor neuroendocrine activity that can contribute to metabolic and agonists immune system disordered. Immuno- Suppress autoimmune response thought to be implicated in Daclizumab, NBI 6024, suppressants Type I and possibly Type II diabetes. Example: Humanized TRX-TolerRx, OKT3-gamma- monoclonal antibody that recognizes and inhibits the alpha 1-ala-ala subunit of IL-2 receptors; humanized Mab that binds to T cell CD3 receptor to block function of T-effector cells that attack the body and cause autoimmune disease Insulin-like Recombinant protein complex of insulin-like growth factor-1 and Somatomedin-1 binding growth binding protein-3; regulates the delivery of somatomedin to target protein 3 factor-1 tissues. Reduces insulitis severity and beta cell destruction agonists Insulin Insulin sensitizers, generally orally active S15261, Dexlipotam, CLX sensitizers 0901, R 483, TAK 654 Growth Mimic the action of native GHRF TH9507, SOM 230 hormone releasing factor agonists Glucagon Inhibit glucagon action, stimulating insulin production and Liraglutide, NN 2501 antagonists secretion, resulting in lower postprandial glucose levels Diabetes type Prevents destruction of pancreatic beta cells that occurs in type 1 Q-Vax, Damyd vaccine 1 vaccine diabetes Sodium- Selectively inhibits the sodium glucose co-transporter, which T 1095 glucose co- mediates renal reabsorption and intestinal absorption of glucose transporter to maintain appropriate blood glucose levels. inhibitor Glycogen Inhibit glycogen phosphorylase, thus slowing release of glucose Ingliforib phosphorylase inhibitors Undefined Drugs that act in ways beneficial to those with Type I or Type II FK 614, INGAP Peptide, R mechanisms Diabetes Mellitus, e.g., by reducing blood glucose and 1439 triglyceride levels, whose mechanisms have not been elucidated. Antisense Bind to RNA and cause its destruction, thereby decreasing ISIS 113715 oligonucleotides protein production from corresponding gene. Insulinotropin Stimulate insulin release CJC 1131 agonists Gluconeogenesis Inhibit gluconeogenesis, thus modulating blood glucose levels CS 917 inhibitors Hydroxysteroid Inhibit hydroxysteroid dehydrogenase, which are responsible for BVT 3498 dehydrogenase excess glucocorticoid production and hence, visceral obesity inhibitors Beta 3 Agonist for beta 3 adrenoceptor, decreases blood glucose and YM 178, Solabegron, adrenoceptor suppresses weight gain N5984, agonist Nitric oxide Decreases effects of NO NOX 700 antagonist Carnitine Inhibits carnitine palmitoyltransferase ST 1326 palmitoyl- transferase inhibitor

TABLE 17 Classes of Drugs for Treatment of Multiple Sclerosis Class Mechanism of Action Examples Recombinant IFN-beta has numerous effects on the immune system. Exact Interferon-beta-1b, interferons mechanism of action in MS not known Interferon-beta-1a Altered peptide Ligands either templated on sequence of myelin basic protein, or Glatiramer acetate, MBP- ligands containing randomly arranged amino acids (e.g., ala, lys, glu, tyr) 8298, Tiplimotide, AG-284 whose structure resembles myelin basic protein, which is thought to be an antigen that plays a role in MS. Bind to the T-cell receptor but do not activate the T-cell because are not presented by an antigen-presenting cell. Chemotherapeutic Immunosuppressive. MS is thought to be an autoimmune Mitoxantrone, agents disease, so chemotherapeutics that suppress immunity improve Methotrexate, MS Cyclophosphamide Immuno- Act via a variety of mechanisms to dampen immune response. Azathioprine, suppressants Teriflunomide, Oral Cladribine Corticosteroids Induce T-cell death and may up-regulate expression of adhesion Methylprednisolone molecules in endothelial cells lining the walls of cerebral vessels, as well as decreasing CNS inflammation. Monoclonal Bind to specific targets in the autoimmune cascade that produces Natalizumab, Daclizumab, Antibodies MS, e.g., bind to activated T-cells Altemtuzumab, BMS- 188667, E-6040, Rituximab, M1 MAbs, ABT 874, T- 0047 Chemokine Prevent chemokines from binding to specific chemokine BX-471, MLN-3897, MLN- Receptor receptors involved in the attraction of immune cells into the CNS 1202 Antagonists of multiple sclerosis patients, and inhibiting immune cell migration into the CNS AMPA AMPA receptors bind glutamate, an excitatory neurotransmitter, E-2007 Receptor which is released in excessive quantities in MS. AMPA Antagonists antagonists suppresses the damage caused by the glutamate Recombinant GGF is associated with the promotion and survival of Recombinant Human GGF2 Human Glial oligodendrocytes, which myelinate neurons of the CNS. rhGGF Growth may help myelinate oligodendrocytes and protect the myelin Factor (GGF) sheath. T-cell Mimic the part of the receptor in T cells that attack myelin NeuroVax Receptor sheath, which activates regulatory T cells to decrease pathogenic Vaccine T-cells. Oral Various effects on the immune response that can modulate the Simvastatin, FTY-720, Oral Immuno- process of MS Glatiramer Acetate, FTY- modulators 720, Pirfenidone, Laquinimod

In one embodiment, detection of miR-326 from a vesicle can be used to characterize multiple sclerosis, and one or more treatments selected from Table 17 can be selected for the subject. In another embodiment, the theranosis can include selecting a therapy such as interferon β-1b and interferon β-1a.

In another embodiment, a treatment can be selected for a subject suffering from Rheumatoid arthritis. One or more biomarkers, such as, but not limited to, 677CC/1298AA MTHFR, 677CT/1298AC MTHFR, 677CT MTHFR, G80AA RFC-1, 3435TT MDR1 (ABCB1), 3435TT ABCB1, AMPD1/ATIC/ITPA, IL1-RN3, HLA-DRB103, CRP, HLA-D4, HLA DRB-1, anti-citrulline epitope containing peptides, anti-A1/RA33, Erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), SAA (serum amyloid-associated protein), rheumatoid factor, IL-1, TNF, IL-6, IL-8, IL-1Ra, Hyaluronic acid, Aggrecan, Glc-Gal-PYD, osteoprotegerin, RNAKL, carilage oligomeric matrix protein (COMP), and calprotectin, can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, Methotrexate, infliximab, adalimumab'etanercept, sulfasalazine, or a combination thereof.

Thus, a treatment can be selected for the subject suffering from an autoimmune condition, based on the biosignature of the subject's vesicle

Infectious Diseases

Assessing a vesicle can be used in the theranosis of an infectious disease such as a bacterial, viral or other infectious condition or disease. An infectious or parasitic disease can arise from bacterial, viral, fungal, or other parasitic infection. For example, the disease or condition may be Whipple's Disease, Prion Disease, cirrhosis, methicillin-resistant staphylococcus aureus, HIV, hepatitis, syphilis, meningitis, malaria, tuberculosis, or influenza.

An infectious or parasitic disease includes, but is not limited to, intestinal infectious diseases, tuberculosis, zoonotic bacterial diseases, other bacterial diseases, human immunodeficiency virus hiv infection, poliomyelitis and other non arthropod borne viral diseases of central nervous system, viral diseases accompanied by exanthem, arthropod borne viral diseases, other diseases due to viruses and chlamydiae, rickettsioses and other arthropod borne diseases, syphilis and other venereal diseases, other spirochetal diseases, mycoses, helminthiases, other infectious and parasitic diseases, and late effects of infectious and parasitic diseases. Intestinal infectious diseases include, but are not limited to cholera, typhoid and paratyphoid fevers, salmonella gastroenteritis, shigellosis, shigellosis unspecified, staphylococcal food poisoning, amoebiasis, acute amoebic dysentery without mention of abscess, chronic intestinal amoebiasis without mention of abscess, amoebic nondysenteric colitis, amoebic liver abscess, amoebic lung abscess, amoebic brain abscess, amoebic skin ulceration, amoebic infection of other sites, unspecified amoebiasis, balantidiasis, giardiasis, coccidiosis, intestinal trichomoniasis, cryptosporidiosis, cyclosporiasis unspecified protozoal intestinal disease, intestinal infections due to other organisms, enteritis due to rotavirus, enteritis due to other viral enteritis, intestinal infection due to other organism not elsewhere classified, ill defined intestinal infections, colitis enteritis and gastroenteritis of presumed infectious origin.

A human immunodeficiency virus infection includes, but is not limited to human immunodeficiency virus infection with specified conditions, human immunodeficiency virus infection causing other specified, and other human immunodeficiency virus infection.

A poliomyelitis and other non arthropod borne viral diseases of central nervous system include, but are not limited to acute poliomyelitis, slow virus infection of central nervous system, kuru, creutzfeld jakob disease, meningitis due to enterovirus, other enterovirus diseases of central nervous system, and other non arthropod borne viral diseases of central nervous system. Viral diseases accompanied by exanthem include, but are not limited to smallpox, cowpox and paravaccinia, chickenpox, herpes zoster, herpes simplex, genital herpes, herpetic gingivostomatitis, herpetic disease, uncomplicated, measles, rubella, other viral exanthemata, fifth disease, unspecified viral exanthems, roseola infantum, other human herpesvirus encephalitis, other human herpesvirus infections, other poxvirus infections, other orthopoxvirus infections, monkeypox, other parapoxvirus infections, bovine stomatitis, sealpox, yatapoxvirus infections, tanapox, yaba monkey tumor virus, other poxvirus infections, and unspecified poxvirus infections.

Arthropod borne viral diseases include, but are not limited to yellow fever, dengue fever, mosquito borne viral encephalitis, encephalitis, mosquito unspecified, tick borne viral encephalitis, viral encephalitis transmitted by other and unspecified arthropods, arthropod borne hemorrhagic fever, ebola unspecified, other arthropod borne viral diseases, and unspecified west nile virus.

Other diseases due to viruses and chlamydiae include, but are not limited to viral hepatitis, hepatitis a with hepatic coma, hepatitis a without coma, hepatitis b with hepatic coma, hepatitis b without coma, acute, other specified viral hepatitis with mention of hepatic coma, other specified viral hepatitis without mention of hepatic coma, unspecified viral hepatitis c, viral hepatitis c without hepatic coma, viral hepatitis c with hepatic coma, hepatitis, viral, rabies, mumps, mumps, uncomplicated, ornithosis, specific diseases due to coxsackie virus, herpangina, hand, foot, mouth disease, mononucleosis, trachoma, other diseases of conjunctiva due to viruses and chlamydiae, other diseases due to viruses and chlamydiae, molluscum contagiosum, warts, all sites, condyloma acuminata, sweating fever, cat scratch disease, foot and mouth disease, cmv disease, viral infection in conditions classified elsewhere and of unspecified site, rhinovirus, hpv, and respiratory syncytial virus. Rickettsioses and other arthropod borne diseases include, but are not limited to louse borne epidemic typhus, other typhus, tick borne rickettsioses, rocky mountain spotted fever, other rickettsioses, malaria, leishmaniasis, trypa omiasis, relapsing fever, other arthropod borne diseases, other specified arthropod borne diseases, lyme disease, and babesiosis.

A viral host includes, but is not limited to Adenovirus, Astrovirus, Avian influenza virus, Coxsackievirus, Dengue virus, Ebola virus, Echovirus, Enteric adenovirus, Enterovirus, Hantaviruses, Hepatitis A virus, Hepatitis B virus, Hepatitis C virus, Hepatitis D virus, Hepatitis E virus, Herpes simplex virus (HSV), Human cytomegalovirus, Human immunodeficiency virus (HIV), Human papillomavirus (HPV), Influenza virus, Japanese encephalitis virus (JEV), Lassa virus, Marburg virus, Measles virus, Mumps virus, Norovirus, Parainfluenza virus, Poliovirus, Rabies virus, Respiratory syncytial virus, Rotavirus, Rubella virus, SARS coronavirus, Tick-borne encephalitis virus (TBEV), Variola virus, West Nile virus, and Yellow fever virus. A fungal host includes, but is not limited to Candida albicans. A parasitic host includes, but is not limited to Plasmodium, Schistosoma mansoni, and Trichomonas vaginalis.

A bacterial host includes, but is not limited to Acinetobacter baumannii, Bacillus anthracis, Bartonella, Bordetella pertussis, Borrelia, Brucella, Chlamydia pneumoniae, Chlamydia trachomatis, Clostridium botulinum, Corynebacterium diphtheriae, Coxiella burnetii, Ehrlichia, Enterococci, Enterovirulent Escherichia coli, Francisella tularensis, Haemophilus ducreyi, Helicobacter pylori, Klebsiella pneumoniae, Legionella pneumophila, Leptospira interrogans, Mycobacterium tuberculosis, Mycoplasma genitalium, Mycoplasma pneumoniae, Neisseria gonorrhoeae, Neisseria meningitidis, Orientia tsutsugamushi, Pseudomonas aeruginosa, Rickettsia, Salmonella, Shigella, Staphylococcus aureus, Streptococcus pneumoniae, Streptococcus pyogenes, Treponema pallidum, Ureaplasma urealyticum, Vibrio cholerae, Vibrio vulnificus, and Yersinia pestis.

Zoonotic bacterial diseases includes, but is not limited to plague, bubonic plague, tularemia, anthrax, brucellosis, glanders, melioidosis, rat bite fever, listeriosis, erysipelothrix infection, and pasteurellosis. Other bacterial diseases include, but are not limited to leprosy, diseases due to other mycobacteria, diphtheria, whooping cough, streptococcal sore throat and scarlatina, strep throat, scarlet fever, erysipelas, meningococcal meningitis, tetanus, septicaemia, pneumococcal septicemia, septicemia, gram negativeunspecified, septicemia, and actinomycotic infections.

Tuberculosis includes, but is not limited to primary tuberculous infection, pulmonary tuberculosis, tuberculosis of meninges and central nervous system, tuberculosis of intestines, peritoneum, and mesenteric glands, tuberculosis of bones and joints, tuberculosis of vertebral column, pott's disease, tuberculosis of genitourinary system, tuberculosis of other organs, erythema nodosum with hypersensitivity reaction in tuberculosis, bazin disease, tuberculosis of peripheral lymph nodes, scrofula, and miliary tuberculosis.

Syphilis and other venereal diseases include, but are not limited to congenital syphilis, early syphilis, symptomatic, syphilis, primary, genital, early syphilis, latent, cardiovascular syphilis, neurosyphilis, other forms of late syphilis, with symptoms, late syphilis, latent, other and unspecified syphilis, gonococcal infections, gonorrhoea, acute, lower gu tract, gonococcal conjunctivitis, and nongonococcal urethritis. Other spirochetal diseases include, but are not limited to leptospirosis, Vincent's angina, yaws, and pinta. Mycoses include, but are not limited to dermatophytosis, dermatophytosis of scalp/beard, onychomycosis, dermatophytosis of hand, tinea cruris, tinea pedis, tinea corporis, dermatomycosis, other and unspecified, tinea versicolor, dermatomycosisunspecified, candidiasis, moniliasis, oral, moniliasis, vulva/vagina, monilial balanitis, moniliasis, skin/nails, coccidioidomycosis, histoplasmosis, histoplasma infection unspecified, blastomycotic infection, other mycoses, and opportunistic mycoses.

Helminthiases include, but are not limited to schistosomiasis bilharziasis, other trematode infections, echinococcosis, other cestode infection, trichi is, filarial infection and dracontiasis, ancylostomiasis and necatoriasis, other intestinal helminthiases, ascariasis, anisakiasis, strongyloidiasis, trichuriasis, enterobiasis, capillariasis, trichostrongyliasis, other and unspecified helminthiases, and unspecified intestinal parasitism. Other infectious and parasitic diseases include, but are not limited to toxoplasmosis, toxoplasmosisunspecified, trichomoniasis, urogenital trichomoniasis, trichomonal vaginitis, trichomoniasis, urethritis, pediculosis and phthirus infestation, pediculosis, head lice, pediculosis, body lice, pediculosis, pubic lice, pediculosisunspecified, acariasis, scabies, chiggers, sarcoidosis, ainhum, behcet's syndrome, pneumocystosis, psorospermiasis, and sarcosporidiosis. Late effects of infectious and parasitic diseases include, but are not limited to late effects of tuberculosis, and late effects of polio.

Infectious Disease: Biosignature

A biosignature of a vesicle can be assessed to provide a theranosis for a subject. The biosignature of the vesicle can comprise one or more biomarkers such as, but not limited to, any one or more biomarkers as described herein, such as, but not limited to, those listed in FIG. 1 for infection diseases, and FIGS. 24 and 43.

In some embodiments, an infectious disease can be characterized by detecting a component of a pathogen, such as a virus, bacteria, or other infectious agent, in a vesicle. For example, the component can be ABC transporters (Candida albicans), ABC transporters (Enterococci), AMA-1 (Apical membrane antigen 1), ATPase, Aac(6′)-Aph(2″) enzyme, Ace (Accessory cholera enterotoxin), Acf (Accessory colonization factor), Acr (α-crystallin) protein, AhpC and AhpD, Amyloid-β, AroC, Attachment glycoprotein (G) (Respiratory syncytial virus), Autolysin (N-acetylmuramoyl-L-alanine amidase), BacA, BmpA (P39), Botulinum neurotoxins, BvgA, -S, and -R, BvrR-BvrS, C4BP (C4b-binding protein), C5a peptidase, CAMP factor (cohemolysin), CBP (Choline binding protein), CME type β-lactamase, CSP (Circumsporozoite protein), CT (cholera toxin), CTX-M metallo-β-lactamase, CagA (cytotoxin-associated antigen), Capsid protein (C) (Dengue virus), Capsid protein (C) (Japanese encephalitis virus), Capsid protein (C) (Tick-borne encephalitis virus), Capsid protein (C) (West Nile virus), Capsid protein (C) (Yellow fever virus), Capsid protein (Astrovirus), Capsid protein (Coxsackievirus), Capsid protein (Echovirus), Capsid protein (Enterovirus), Capsid protein (Hepatitis A virus), Capsid protein (Poliovirus), Capsid protein (Rotavirus), Catechol siderophore ABC transporter, Com-1, CrmB (Cytokine response modifier), Cytolysin, D-Ala-D-Lac ligase, DHFR (Dihydrofolate reductase), DHPS (Dihydropteroate synthetase), DbpA (Decorin-binding protein A), Diphtheria toxin, Dot/Icm complex, E1 and E2 proteins (Rubella virus), E1A protein (Adenovirus), E1A protein (Enteric adenovirus), E1B protein (Adenovirus), E1B protein (Enteric adenovirus), E2 early transcription region 2, E3 protein (Adenovirus), E4 protein (Adenovirus), E6 early transcription region 6, E7 early transcription region 7, EF (Edema factor), ESAT-6 and CFP-10, Elastase (Vibrio vulnificus), Env, Envelope glycoprotein (E) (Dengue virus), Envelope glycoprotein (E) (Japanese encephalitis virus), Envelope glycoprotein (E) (Tick-borne encephalitis virus), Envelope glycoprotein (E) (West Nile virus), Envelope glycoprotein (E) (Yellow fever virus), Esp (Enterococcal surface protein), Esp (Type III System-Secreted Proteins), F1 capsule (F1 antigen), FH (Factor H), FHA (Filamentous hemagglutinin), Falcipain 1/2, Fiber protein (Adenovirus), Fiber protein (Enteric adenovirus), Fibronectin binding protein II (Protein F/sfbII) (Streptococcus pyogenes), Fibronectin binding protein (Leptospira interrogans), Fibronetin binding protein (FBP54) (Streptococcus pyogenes), Fimbrial protein, Flagellin (FlaB and -A) (H. pylori), Flagellin (H-antigen) (Escherichia coli), Flagellin (H-antigen) (Salmonella), Flagellin (Vibrio vulnificus), FopA (43 kDa lipoprotein), Fusion protein (F) (Mumps virus), Fusion protein (F) (Parainfluenza virus), Fusion protein (F) (Respiratory syncytial virus), G6PD (Glucose-6-phosphate dehydrogenase), GES (Guiana extended-spectrum β-lactamase), GTP cyclohydrolase, Gag, Glycoprotein (G) (Rabies virus), Glycoprotein (GP) (Ebola virus), Glycoprotein (GP) (Lassa virus), Glycoprotein (GP) (Marburg virus), Glycoproteins (Gn/Gc) (Hantaviruses), HMW (Cytadherence accessory protein), HRP2 (Histidine-rich protein 2), Hemagglutinin (Avian influenza virus), Hemagglutinin (Influenza virus), Hemagglutinin (Measles virus), Hemagglutinin (Variola virus), Hemagglutinin-esterase glycoprotein (HE), Hemagglutinin-neuraminidase (HN) (Mumps virus), Hemagglutinin-neuraminidate (HN) (Paraninfluenza virus), Hemolysin (Vvh), Hexon protein (Adenovirus), Hexon protein (Enteric adenovirus), Hsp60 (Heat shock protein 60), Hyaluronate lyase, Hyaluronidase, IMP metallo-13-lactamase (Acinetobacter baumannii), IMP metallo-β-lactamase (Klebsiella pneumoniae), IcsA and IcsB, IgA protease (Neisseria gonorrhoeae), IgA1 protease (Streptococcus pneumoniae), IgG and IgM for HSV 1/2, InhA, Intimin, InvA (Rickettsia), Invasin (Escherichia coli), Invasin (Yersinia pestis), IpaA, -B, -C, -D and -H, KPC metallo-β-lactamase, KatG, L protein (Lassa virus), L1 late transcription region 1, LF (Lethal factor), LSA1 (Liver-stage antigen 1), LT (heat labile toxin), LcrV (V antigen), LigA and LigB, Lipoprotein, M protein, MSP (Merozoite surface protein), Matrix protein (M) (Rabies virus), Matrix protein (M) (Respiratory syncytial virus), Matrix protein (Avian influenza virus), Matrix protein (Influenza virus), MexAB-OprM, MexCD-OprJ, MexEF-OprN, MexXY-OprM, Mip (Macrophage infectivity potentiator), NSE (Neuron-specific enolase), Nef, Neuraminidase (Avian influenza virus), Neuraminidase (Influenza virus), Neuraminidase (Streptococcus pneumoniae), Non-structural protein (NS) (Respiratory syncytial virus), Non-structural protein 1 (NS1) (Dengue virus), Non-structural protein 1 (NS1) (Japanese encephalitis virus), Non-structural protein 1 (NS1) (Tick-borne encephalitis virus), Non-structural protein 1 (NS1) (West Nile virus), Non-structural protein 1 (NS1) (Yellow fever virus), Non-structural protein 2A (NS2A) (Dengue virus), Non-structural protein 2A (NS2A) (Japanese encephalitis virus), Non-structural protein 2A (NS2A) (Tick-borne encephalitis virus), Non-structural protein 2A (NS2A) (West Nile virus), Non-structural protein 2A (NS2A) (Yellow fever virus), Non-structural protein 2B (NS2B) (Dengue virus), Non-structural protein 2B (NS2B) (Japanese encephalitis virus), Non-structural protein 2B (NS2B) (Tick-borne encephalitis virus), Non-structural protein 2B (NS2B) (West Nile virus), Non-structural protein 2B (NS2B) (Yellow fever virus), Non-structural protein 3 (NS3) (Dengue virus), Non-structural protein 3 (NS3) (Japanese encephalitis virus), Non-structural protein 3 (NS3) (Tick-borne encephalitis virus), Non-structural protein 3 (NS3) (West Nile virus), Non-structural protein 3 (NS3) (Yellow fever virus), Non-structural protein 4 (Rotavirus), Non-structural protein 4A (NS4A) (Dengue virus), Non-structural protein 4A (NS4A) (Japanese encephalitis virus), Non-structural protein 4A (NS4A) (Tick-borne encephalitis virus), Non-structural protein 4A (NS4A) (West Nile virus), Non-structural protein 4A (NS4A) (Yellow fever virus), Non-structural protein 4B (NS4B) (Dengue virus), Non-structural protein 4B (NS4B) (Japanese encephalitis virus), Non-structural protein 4B (NS4B) (Tick-borne encephalitis virus), Non-structural protein 4B (NS4B) (West Nile virus), Non-structural protein 4B (NS4B) (Yellow fever virus), Non-structural protein 5 (N55) (Dengue virus), Non-structural protein 5 (N55) (Japanese encephalitis virus), Non-structural protein 5 (N55) (Tick-borne encephalitis virus), Non-structural protein 5 (N55) (West Nile virus), Non-structural protein 5 (N55) (Yellow fever virus), Non-structural proteins (Avian influenza virus), Non-structural proteins (Influenza virus), Nucleocapsid (Hantaviruses), Nucleocapsid (Measles virus), Nucleocapsid (Parainfluenza virus), Nucleocapsid (SARS coronavirus), Nucleoprotein (N) (Rabies virus), Nucleoprotein (NP) (Respiratory syncytial virus), Nucleoprotein (major nucleoprotein) (Marburg virus), Nucleoprotein (Avian influenza virus), Nucleoprotein (Ebola virus), Nucleoprotein (Influenza virus), Nucleoprotein (Lassa virus), ORF 1 (Hepatitis E virus), ORF2 (Hepatitis E virus), ORF3 (Hepatitis E virus), OXA metallo-β-lactamase (Acinetobacter baumannii), OXA metallo-β-lactamase (Klebsiella pneumoniae), OmpA and OmpB (Rickettsia), OmpL1 (Leptospira interrogans), OmpQ (Outer membrane porin protein) (Bordetella pertussis), OmpS (Legionella pneumophila), Opacity factor, OprD, Osp (Outer surface protein), Outer membrane proteins (Chlamydia pneumoniae), Outer membrane proteins (Ehrlichia), P1 adhesin, P30 adhesin, PA (Protective antigen), PBP (Penicillin-binding protein), PCRMP 1-4 (Cysteine repeat modular proteins), PER metallo-β-lactamase, Pat1, Peptidoglycan (murein) hydrolase, Pertactin (p69), Pertussis toxin, PfEMP1 (Plasmodium falciparum erythrocyte membrane protein-1), Phosphoprotein (P) (Respiratory syncytial virus), Phosphoprotein (Measles virus), Pla (plasminogen activator), Plasminogen-binding protein, Pld, Pneumolysin, Pol, Poly-D-glutamic acid capsule, Polymerase (L) (Rabies virus), Porin, Premembrane/membrane protein (PrM/M) (Dengue virus), Premembrane/membrane protein (PrM/M) (Japanese encephalitis virus), Premembrane/membrane protein (PrM/M) (Tick-borne encephalitis virus), Premembrane/membrane protein (PrM/M) (West Nile virus), Premembrane/membrane protein (PrM/M) (Yellow fever virus), Proteins for two-component regulatory systems (Ehrlichia), Proteins for two-component regulatory systems (Mycobacterium tuberculosis), Proteins of gB, gC, gD, gH, and gL, PsaA, PspA (Pneumococcal surface protein A), PurE, Pyrogenic exotoxins, RBP 1/2 (Reticulocyte binding protein 1/2), RdRp (RNA dependant RNA polymerase) (Norovirus), RdRp (RNA dependent RNA polymerase) (Astrovirus), RdRp (RNA dependent RNA polymerase) (SARS coronavirus), Rev, RfbE, RibD and RibE, Rmp, S-layer protein, S100B (S100 protein 3 chain), SHV metallo-β-lactamase, SIM metallo-β-lactamase, ST (heat stable toxin), Salmonella plasmid virulence (SPV) proteins, Serine protease (Astrovirus), ShET1/2, Shiga toxin (Verotoxin), SipA (Salmonella Invasion Protein A), SlyA, Small hydrophobic protein, Sop (Salmonella outer protein), Spike glycoprotein (S), Streptococcal DNase, Streptogramin A acetyltransferase, Streptokinase, Streptolysin O, StxA/B (Shiga toxin A/B), SucB (Dihydrolipoamide succinyltransferase) (Mycobacterium tuberculosis), SucB (dihydrolipoamide succinyltransferase) (Coxiella burnetii), Syc (Yop chaperones), T protein, TCP (toxin-coregulated pilus), TEM metallo-β-lactamase, TRAP (Thrombospondin-related anonymous protein), Tat, Tau-protein, TcfA (Tracheal colonization factor), Tir (Translocated intimin receptor), TlyA and TlyC, ToxR (toxin regulatory protein), Tul4 (17 kDa lipoprotein), Type W pili, Urease (Brucella), Urease (Helicobacter pylori), VEB metallo-β-lactamase, VETF (Virus early transcription factor), VIM metallo-β-lactamase (Acinetobacter baumannii), VIM metallo-β-lactamase (Klebsiella pneumoniae), VP1 (Norovirus), VP2 (Norovirus), VP24 (Ebola virus), VP24 (Marburg virus), VP30 (minor nucleoprotein) (Ebola virus), VP30 (minor nucleoprotein) (Marburg virus), VP35 (P-like protein) (Ebola virus), VP35 (P-like protein) (Marburg virus), VP40 (Matrix Protein) (Ebola virus), VP40 (Matrix Protein) (Maburg virus), VacA (vacuolating cytotoxin), Vag8 (virulence-activated gene 8), Vif, VirB type IV secretion system, VlsE (35 kDa lipoprotein), Vpr, Vpu/Vpx, XerD, Yops (Yersinia outermembrane proteins), Ysc (Yop secretion apparatus), Z protein (Lassa virus), Zot (zonula occuldens toxin), gG1 (HSV-1) and gG2 (HSV-2), p41i, p83, and p100, pLDH (Plasmodium lactate dehydrogenase), α/β/γ proteins, 120 kDa gene, 16S and 5S rRNA genes (Legionella pneumophila), 16S rRNA (Bartonella), 16S rRNA (Borrelia), 16S rRNA (Brucella), 16S rRNA (Ehrlichia), 16S rRNA (Klebsiella pneumoniae), 16S rRNA (Orientia tsutsugamushi), 16S rRNA (Rickettsia), 16S rRNA gene (Acinetobacter baumannii), 16S rRNA gene (Chlamydia pneumoniae), 16S rRNA gene (Clostridium botulinum), 16S rRNA gene (Mycoplasma pneumoniae), 16S rRNA gene (Neisseria gonorrhoeae), 16S rRNA gene (Vibrio vulnificus), 16S-23S rRNA intergenic spacer (Bartonella), 16S-23S rRNA intergenic spacer (Coxiella burnetii), 17 kDa gene, 18S ssrRNA, 23S rRNA gene (Acinetobacter baumannii), 23S rRNA gene (Neisseria gonorrhoeae), 2C gene, 3′ NCR (Dengue virus), 3′ NCR (Japanese encephalitis virus), 3′ NCR (Tick-borne encephalitis virus), 3′ NCR (West Nile virus), 3′ NCR (Yellow fever virus), 5′ NCR (Coxsackievirus), 5′ NCR (Dengue virus), 5′ NCR (Echovirus), 5′ NCR (Enterovirus), 5′ NCR (Japanese encephalitis virus), 5′ NCR (Polioviurs), 5′ NCR (Tick-borne encephalitis virus), 5′ NCR (West Nile virus), 5′ NCR (Yellow fever virus), 56 kDa gene, A13L gene, ARE1 gene, ATF2 gene, B12R gene, B6R gene, B8R gene, C gene (Dengue virus), C gene (Japanese encephalitis virus), C gene (Tick-borne encephalitis virus), C gene (West Nile virus), C gene (Yellow fever virus), C3L gene, CDR 1/2 genes, E gene (Dengue virus), E gene (Japanese encephalitis virus), E gene (Tick-borne encephalitis virus), E gene (West Nile virus), E gene (Yellow fever virus), E1 and E2 genes, E1A gene (Adenovirus), E1A gene (Enteric adenovirus), E1B gene (Adenovirus), E1 B gene (Enteric adenovirus), E2 gene, E3 gene (Adenovirus), E3L gene, E4 gene (Adenovirus), E6 gene, E7 gene, ERG genes, ESAT-6 and CFP-10 genes, F gene (Mumps virus), F gene (Parainfluenza virus), F gene (Respiratory syncytial virus), G gene (Rabies virus), G gene (Respiratory syncytial virus), GP gene (Ebola virus), GP gene (Lassa virus), GP gene (Marburg virus), H gene (Measles virus), HA gene (Avian influenza virus), HA gene (Influenza virus), HE gene (SARS Coronavirus), HN gene (Mumps virus), HN gene (Parainfluenza virus), IS100, IS1081, IS1533 (Leptospira interrogans), IS285, IS481 (BP0023), IS6110, IS711 (Brucella), ISFtu, J7R gene, L gene (Lassa virus), L gene (Rabies virus), L segment, L1 gene, LEE (locus of enterocyte effacement), Long control region (LCR), M gene (Rabies virus), M gene (Respiratory syncytial virus), M genes (Avian influenza virus), M genes (Influenza virus), M segment, MDR1 gene, MEC3 gene, N gene (Measles virus), N gene (Rabies virus), N gene (SARS coronavirus), NA gene (Avian influenza virus), NA gene (Influenza virus), NC gene (Parainfluenza virus), NP gene (Avian influenza virus), NP gene (Ebola virus), NP gene (Influenza virus), NP gene (Lassa virus), NP gene (Marburg virus), NP gene (Respiratory syncytial virus), NS gene (Avian influenza virus), NS gene (Influenza virus), NS gene (Respiratory syncytial virus), NS1 gene (Dengue virus), NS1 gene (Japanese encephalitis virus), NS1 gene (Tick-borne encephalitis virus), NS1 gene (West Nile virus), NS1 gene (Yellow fever virus), NS2A gene (Dengue virus), NS2A gene (Japanese encephalitis virus), NS2A gene (Tick-borne encephalitis virus), NS2A gene (West Nile virus), NS2A gene (Yellow fever virus), NS2B gene (Dengue virus), NS2B gene (Japanese encephalitis virus), NS2B gene (Tick-borne encephalitis virus), NS2B gene (West Nile virus), NS2B gene (Yellow fever virus), NS3 gene (Dengue virus), NS3 gene (Japanese encephalitis virus), NS3 gene (Tick-borne encephalitis virus), NS3 gene (West Nile virus), NS3 gene (Yellow fever virus), NS4 gene (Rotavirus), NS4A gene (Dengue virus), NS4A gene (Japanese encephalitis virus), NS4A gene (Tick-borne encephalitis virus), NS4A gene (West Nile virus), NS4A gene (Yellow fever virus), NS4B gene (Dengue virus), NS4B gene (Japanese encephalitis virus), NS4B gene (Tick-borne encephalitis virus), NS4B gene (West Nile virus), NS4B gene (Yellow fever virus), NS5 gene (Dengue virus), NS5 gene (Japanese encephalitis virus), NS5 gene (Tick-borne encephalitis virus), NS5 gene (West Nile virus), NS5 gene (Yellow fever virus), ORF 1a (Astrovirus), ORF 1b (Astrovirus), ORF 2 (Astrovirus), ORF1 (Hepatitis E virus), ORF1 (Norovirus), ORF2 (Hepatitis E virus), ORF2 (Norovirus), ORF3 (Hepatitis E virus), ORF3 (Norovirus), P gene (Measles virus), P gene (Respiratory syncytial virus), PDH1 gene, Peptidyltransferase mutations, Plasmids (QpH1, QpRS, QpDG, QpDV), PrM/M gene (Dengue virus), PrM/M gene (Japanese encephalitis virus), PrM/M gene (Tick-borne encephalitis virus), PrM/M gene (West Nile virus), PrM/M gene (Yellow fever virus), RdRp gene in ORF 1 ab (SARS coronavirus), S gene (SARS coronavirus), S segment, SH gene (Mumps virus), SNP (single nucleotide polymorphism), Salmonella pathogenicity island (SPI), Salmonella plasmid virulence (SPV) operon, ShET1/2 genes, VNTR (variable number tandem repeat) (Bacillus anthracis), VNTR (variable number tandem repeat) (Brucella), VNTR (variable number tandem repeat) (Francisella tularensis), VNTR (variable number tandem repeat) (Yersinia pestis), VP24 gene (Ebola virus), VP24 gene (Marburg virus), VP30 gene (Ebola virus), VP30 gene (Marburg virus), VP35 gene (Ebola virus), VP35 gene (Marburg virus), VP40 gene (Ebola virus), VP40 gene (Marburg virus), Z gene (Lassa virus), aac(3) gene, aac(6′) gene, aac(6′)-aph(2″) gene, aad gene, ace gene, acpA gene, agrBDCA locus, ahpC and ahpD genes, arlRS locus, atxA gene, bclA gene, blaCTX-M gene, blaGES gene, blaGIM gene (Pseudomonas aeruginosa), blaIMP gene (Acinetobacter baumannii), blaIMP gene (Klebsiella pneumoniae), blaIMP gene (Pseudomonas aeruginosa), blaKPC gene, blaOXA gene (Acinetobacter baumannii), blaOXA gene (Klebsiella pneumoniae), blaOXA gene (Pseudomonas aeruginosa), blaSHV gene, blaSIM gene (Klebsiella pneumoniae), blaSIM gene (Pseudomonas aeruginosa), blaTEM gene, blaVIM gene (Acinetobacter baumannii), blaVIM gene (Klebsiella pneumoniae), blaVIM gene (Pseudomonas aeruginosa), bvg locus (bvgA, -S, and -R genes), cagA gene, cap locus (capB, -C, and -A genes) (Bacillus anthracis), cap operon (capB and -C) (Francisella tularensis), capsid gene (Coxsackievirus), capsid gene (Echovirus), capsid gene (Enterovirus), capsid gene (Hepatitis A virus), capsid gene (Poliovirus), capsid gene (Rotavirus), cme gene, cnt genes, com-1 gene, cppB gene, cps gene, crmB gene, ctx gene, cya gene, cyl gene, eaeA gene, east gene (Escherichia coli), env gene, ery gene, esp gene (Enterococci), esp genes (Escherichia coli), fiber gene (Adenovirus), fiber gene (Enteric adenovirus), fimbriae genes, flaB gene (Borrelia), flaB gene (Leptospira interrogans), flagellin genes, fljA, fljB, and fliC genes, fopA gene, ftsZ gene, gG1 and gG2 gens, gag gene, genes for two-component regulatory systems, genes of gB, gC, gD, gH, and gL, gerX locus (gerXC, -A, and -B genes), glpQ gene, gltA (citrate synthase) gene (Bartonella), gltA (citrate synthase) gene (Rickettsia), groEL gene (Bartonella), groEL gene (Orientia tsutsugamushi), groESL gene (Chlamydia pneumoniae), gyrA and gyrB genes (Pseudomonas aeruginosa), gyrA gene (Neisseria gonorrhoeae), gyrB gene (Bacillus anthracis), hexon gene (Adenovirus), hexon gene (Enteric adenovirus), hin gene, hlyA gene, hmw genes, hspX (Rv2031c) gene, htpAB associated repetitive element (IS1111a), hyl gene, icsA and icsB genes, ileS gene, inhA gene, inv gene (Escherichia coli), inv gene (Salmonella), ipaA, -B, -C, -D and -H genes, katG gene, lef gene, letA gene, lidA gene, lpsB gene, lrgAB locus, luxS gene, lytA gene, lytRS locus, mecA gene, mglA gene, mgrA (rat) gene, mip gene, mtgA gene, mucZ gene, multigene families, mupA gene, nanA and nanB genes, nef gene, omp genes (Brucella), omp genes (Chlamydia pneumonia), ompA and B gene (Rickettsia), ompQ gene, opa genes, osp genes, p1 gene, p30 gene, pagA gene, pap31 gene, parC and parE genes (Pseudomonas aeruginosa), parC gene (Neisseria gonorrhoeae), per gene, pilQ gene, ply gene, pmm gene, pol gene, porA and porB genes, prn4 (pertactin) gene, psaA gene, pspA gene, pst1 fragment and HL-1/HR-1 primers, ptx (promoter region and complete gene), rap 1/2 genes, rev gene, rpo18 gene, rpoB gene, rpoS gene, rpsL gene, rrf(5S)-rrl(23S) intergenic spacer, rsk gene, rtx gene (Vibrio vulnificus), rtxA gene (Legionella pneumophila), sap gene (Bacillus anthracis), sar gene, satA (vatD) and satG (vatE) genes, sca4 gene, secY gene, stx (vt) gene, stxA/B (stx1/2) gene, sucB gene, tat gene, tcp gene, tir gene, tox gene, toxR gene, tu14 gene, urease genes, vacA gene, van A-E genes, veb gene, vif gene, viuB gene, vpr gene, vpu/vpx gene, vvh (Vibrio vulnificus hemolysin) gene, vvpE (Vibrio vulnificus elastase) gene, wboA gene, wzy (O-antigen polymerase) gene, zot gene, α/β/γ genes, C-polysaccharide (rhamnose/N-acetylglucosamine), CPS (capsular polysaccharide), Cyclic β-1,2 glucan, Hyaluronic acid capsule, LPS (lipopolysaccharide) (Bartonella), LPS (lipopolysaccharide) (Brucella), LPS (lipopolysaccharide) (Coxiella burnetii), LPS (lipopolysaccharide) (Rickettsia), LPS (lipopolysaccharide) (Vibrio vulnificus), O-antigen (Escherichia coli), O-antigen (Salmonella), O-antigen (Vibrio cholerae), Vi-antigen (Salmonella), or Catechol siderophore.

Infectious Disease: Standards of Care

Determining the biosignature of a vesicle, the amount of vesicles, or both, of a sample from a subject suffering from an infectious or parasitic disease, disorder or disease can be used to select a standard of care for the subject. An infectious or parasitic disease can be treated according to symptoms associated with the condition. The standard of care includes, for example, treating with one or more antibiotics and antiviral agents.

An antibiotic includes, but not limited to, Amikacin, Gentamicin, Kanamycin, Neomycin, Netilmicin, Streptomycin, Tobramycin, Paromomycin, Geldanamycin, Herbimycin, Loracarbef, Ertapenem, Doripenem, Imipenem/Cilastatin, Meropenem, Cefadroxil, Cefazolin, Cefalotin or Cefalothin, Cefalexin, Cefaclor, Cefamandole, Cefoxitin, Cefprozil, Cefuroxime, Cefixime, Cefdinir, Cefditoren, Cefoperazone, Cefotaxime, Cefpodoxime, Ceftazidime, Ceftibuten, Ceftizoxime, Ceftriaxone, Cefepime, Ceftobiprole, Teicoplanin, Vancomycin, Azithromycin, Clarithromycin, Dirithromycin, Erythromycin, Roxithromycin, Troleandomycin, Telithromycin, Spectinomycin, Aztreonam, Amoxicillin, Ampicillin, Azlocillin, Carbenicillin, Cloxacillin, Dicloxacillin, Flucloxacillin, Mezlocillin, Meticillin, Nafcillin, Oxacillin, Penicillin, Piperacillin, Ticarcillin, Bacitracin, Colistin, Polymyxin B, Ciprofloxacin, Enoxacin, Gatifloxacin, Levofloxacin, Lomefloxacin, Moxifloxacin, Norfloxacin, Ofloxacin, Trovafloxacin, Grepafloxacin, Sparfloxacin, Temafloxacin, Mafenide, Sulfonamidochrysoidine, Sulfacetamide, Sulfanilimide, Sulfasalazine, Sulfisoxazole, Trimethoprim, Trimethoprim-, Sulfamethoxazole, Demeclocycline, Doxycycline, Minocycline, Oxytetracycline, Tetracycline, Sulfadiazine, Sulfamethizole, Arsphenamine, Chloramphenicol, Clindamycin, Lincomycin, Ethambutol, Fosfomycin, Fusidic acid, Furazolidone, Isoniazid, Linezolid, Metronidazole, Mupirocin, Nitrofurantoin, Platensimycin, Pyrazinamide, Quinupristin or Dalfopristin, Rifampicin, Thiamphenicol, Timidazole, Dapsone, and Clofazimine. Examples of antibiotics are also listed in Table 18.

An antiviral agent includes, but is not limited to Abacavir, Aciclovir, Acyclovir, Adefovir, Amantadine, Amprenavir, Ampligen, Arbidol, Atazanavir, Atripla, Boceprevir, Cidofovir, Combivir, Darunavir, Delavirdine, Didanosine, Docosanol, Edoxudine, Efavirenz, Emtricitabine, Enfuvirtide, Entecavir, Famciclovir, Fomivirsen, Fosamprenavir, Foscarnet, Fosfonet, Ganciclovir, Ibacitabine, Immunovir, Idoxuridine, Imiquimod, Indinavir, Inosine, Interferon type III, Interferon type II, Interferon type I, Lamivudine, Lopinavir, Loviride, Maraviroc, Moroxydine, Nelfinavir, Nevirapine, Nexavir, Oseltamivir, Peginterferon alfa-2a, Penciclovir, Peramivir, Pleconaril, Podophyllotoxin, Raltegravir, Ribavirin, Rimantadine, Ritonavir, Pyramidine, Saquinavir, Stavudine, Tea tree oil, Tenofovir, Tenofovir disoproxil, Tipranavir, Trifluridine, Trizivir, Tromantadine, Truvada, Valaciclovir, Valganciclovir, Vicriviroc, Vidarabine, Viramidine, Zalcitabine, Zanamivir, and Zidovudine.

TABLE 18 Examples of Antibiotic Drugs and their Structure Class Structure Class Examples of Antibiotics within Structure Class Amino Acid Azaserine, Bestatin, Cycloserine, 6-diazo-5-oxo- Derivatives L-norleucine Aminoglycosides Armastatin, Amikacin, Gentamicin, Hygromicin, Kanamycin, Streptomycin Benzochinoides Herbimycin Carbapenems Imipenem, Meropenem Coumarin- Novobiocin glycosides Fatty Acid Cerulenin Derivatives Glucosamines 1-deoxynojirimycin Glycopeptides Bleomycin, Vancomycin Imidazoles Metroidazole Penicillins Benzylpenicillin, Benzathine penicillin, Amoxycillin, Piperacillin Macrolides Amphotericin B, Azithromycin, Erythromycin Nucleosides Cordycepin, Formycin A, Tubercidin Peptides Cyclosporin A, Echinomycin, Gramicidin Peptidyl Blasticidine, Nikkomycin Nucleosides Phenicoles Chloramphenicol, Thiamphenicol Polyethers Lasalocid A, Salinomycin Quinolones 8-quinolinol, Cinoxacin, Ofloxacin Steroids Fusidic Acid Sulphonamides Sulfamethazine, Sulfadiazine, Trimethoprim Tetracyclins Oxytetracyclin, Minocycline, Duramycin

In one embodiment, a subject has an HIV infection. One or more biomarkers, such as, but not limited to p24 antigen, TNF-alpha, TNFR-11, CD3, CD14, CD25, CD27, Fas, FasL, beta2 microglobulin, neopterin, HIV RNA, and HLA-B*5701, can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, Zidovudine, Didanosine, Zalcitabine, Stavudine, Lamivudine, Saquinavir, Ritonavir, Indinavir, Nevirane, Nelfinavir, Delavirdine, Stavudine, Efavirenz, Etravirine, Enfuvirtide, Darunavir, Abacavir, Amprenavir, Lonavir/Ritonavirc, Tenofovir, Tipranavir, or a combination thereof.

Thus, a treatment can be selected for the subject suffering from an infectious disease or condition, based on the biosignature of the subject's vesicle.

Neurology

Assessing a vesicle can be used in the theranosis of a neurological disease, such as Multiple Sclerosis (MS), Parkinson's Disease (PD), Alzheimer's Disease (AD) (non-inflammatory and inflammatory), schizophrenia, bipolar disorder, depression, autism, Prion Disease, Pick's disease, dementia, Huntington disease (HD), Down's syndrome, cerebrovascular disease, Rasmussen's encephalitis, viral meningitis, neurospsychiatric systemic lupus erythematosus (NPSLE), amyotrophic lateral sclerosis, Creutzfeldt-Jacob disease, Gerstmann-Straussler-Scheinker disease, transmissible spongiform encephalopathy, ischemic reperfusion damage (e.g. stroke), brain trauma, microbial infection, or chronic fatigue syndrome.

A neurological disorder includes, but is not limited to inflammatory diseases of the central nervous system, hereditary and degenerative diseases of the central nervous system, pain, other headache syndromes, other disorders of the central nervous system, and disorders of the peripheral nervous system. Inflammatory diseases of the central nervous system include, but are not limited to bacterial meningitis, meningitis, hemophilus, meningitis, bacterial, meningitis due to other organisms, cryptococcal meningitis, meningitis of unspecified cause, encephalitis, myelitis, and encephalomyelitis, postinfectious encephalitis, unspecified encephalitis, intracranial and intraspinal abscess, phlebitis and thrombophlebitis of intracranial venous sinuses, venous sinus thrombosis, intracranial, late effects of intracranial abscess or pyogenic infection, sleep disorders, unspecified organic insomnia, insomnia due to medical condition classified elsewhere, and insomnia due to mental disorder. Hereditary and degenerative diseases of the central nervous system include, but are not limited to cerebral degenerations usually manifest in childhood, leukodystrophy, krabbe disease, pelizaeus merzbacher disease, cerebral lipidoses, tay sacks disease, other cerebral degenerations, alzheimer's, pick's disease, senile degeneration of brain, communicating hydrocephalus, obstructive hydrocephalus, idiopathic normal pressure hydrocephalus, other cerebral degeneration, reye's syndrome, dementia with lewy bodies, mild cognitive impairment, so stated, Parkinson's Disease, parkinsonism, primary, other extrapyramidal disease and abnormal movement disorders, other degenerative diseases of the basal ganglia, olivopontocerebellar atrophy, shydrager syndrome, essential tremor/familial tremor, myoclonus, lafora's disease, unverricht disease, Huntington's chorea, fragments of torsion dystonia, blepharospasm, other and unspecified extrapyramidal diseases and abnormal movement disorders, other extrapyramidal diseases and abnormal movement disorders, restless legs, serotonin syndrome, spinocerebellar disease, friedreich's ataxia, spinocerebellar ataxia, hereditary spastic paraplegia, primary cerebellar degeneration, other cerebellar ataxia, cerebellar ataxia in diseases classified elsewhere, other spinocerebellar diseases, ataxia telangiectasia, corticostriatal spinal degeneration, unspecified spinocerebellar disease, anterior horn cell disease, motor neuron disease, amyotrophic lateral sclerosis, progressive muscular atrophy, progressive bulbar palsy, pseudobulbar palsy, primary lateral sclerosis, other motor neuron diseases, other diseases of spinal cord, syringomyelia and syringobulbia, disorders of the autonomic nervous system, idiopathic peripheral autonomic neuropathy, unspecified idiopathic peripheral autonomic neuropathy, carotid sinus syndrome, other idiopathic peripheral autonomic neuropathy, peripheral autonomic neuropathy in disorders classified elsewhere, reflex sympathetic dystrophy, autonomic dysreflexia, and unspecified disorder of autonomic nervous system.

Pain includes, but is not limited to, central pain syndrome, acute pain, chronic pain, neoplasm related pain acute chronic and chronic pain syndrome. Other headache syndromes include, but are not limited to cluster headaches and other trigeminal autonomic cephalgias, unspecified cluster headache syndrome, episodic cluster headache, chronic cluster headache, episodic paroxysmal hemicrania, chronic paroxysmal hemicrania, short lasting unilateral neuralgiform headache with conjunctival injection and tearing, other trigeminal autonomic cephalgias, tension type headache, unspecified tension type headache, episodic tension type headache, chronic tension type headache, post traumatic headache, unspecified post traumatic headache, acute post traumatic headache, chronic post traumatic headache, drug induced headache, not elsewhere classified, complicated headache syndromes, hemicrania continua, new daily persistent headache, primary thunderclap headache, other complicated headache syndrome, other specified headache syndromes, hypnic headache, headache associated with sexual activity, primary cough headache, primary exertional headache, and primary stabbing headache.

Other disorders of the central nervous system include, but are not limited to multiple sclerosis, other demyelinating diseases of central nervous system, neuromyelitis optica, schilder's disease, acute myelitis transverse myelitis, hemiplegia, hemiplegia, flaccid, hemiplegia, spastic, infantile cerebral palsy, cerebral palsy, paraplegic, congenital, cerebral palsy, hemiplegic, congenital, cerebral palsy, quadriplegic, other paralytic syndromes, quadraplegia and quadraparesis, paraplegia, diplegia of upper limbs, monoplegia of lower limb, monoplegia of upper limb, unspecified monoplegia, cauda equina syndrome, other specified paralytic syndromes, locked in state, epilepsy, intractable epilepsy, tonic clonic epilepsy without status, epilepsy with status, epilepsy on temporal lobe without status, unspecified epilepsy without status, migraine, classical not intractable migraine, common but not intractable migraine, not intractable cluster headache, unspecified but, not intractable migraine, cataplexy and narcolepsy, narcolepsy without cataplexy, cerebral cysts, anoxic brain damage, pseudotumor cerebri, unspecified encephalopathy, metabolic encephalopathy, compression of brain, cerebral edema, post spinal puncture, post dural puncture headache, cerebrospinal fluid rhinorrhea, and toxic encephalopathy.

Disorders of the peripheral nervous system include, but are not limited to trigeminal nerve disorders, trigeminal neuralgia, facial nerve disorders, bell's palsy, disorders of other cranial nerves, nerve root and plexus disorders, thoracic outlet syndrome, phantom limb, mononeuritis of upper limb and mononeuritis multiplex, carpal tunnel, mononeuritis of lower limb, lesion of sciatic nerve, meralgia paresthetica, other lesion of femoral nerve, lesion of lateral popliteal nerve, lesion of medial popliteal nerve, tarsal tunnel syndrome, lesion of plantar nerve, morton's neuroma, unspecified mononeuritis of lower limb, mononeuritis of unspecified site, hereditary and idiopathic peripheral neuropathy, inflammatory and toxic neuropathy, guillain barré syndrome, poly neuropathy, alcoholic poly neuropathy, myoneural disorders, myasthenia gravis with exacerbation, myasthenia gravis without exacerbation, muscular dystrophies and other myopathies, benign congenital myopathy, central core disease, centronuclear myopathy, myotubular myopathy, nemaline body disease, and hereditary muscular dyst.

A biosignature of a vesicle can be assessed to provide a theranosis for a subject. The biosignature of the vesicle can comprise one or more biomarkers such as, but not limited to, a biomarker such as those disclosed in the following table:

Neurology: Biosignature

A biosignature of a vesicle can be assessed to provide a theranosis for a subject. The biosignature of the vesicle can comprise one or more biomarkers such as, but not limited to, a biomarker such as listed in FIGS. 1, 45, 46, 47, 48, and 49. The biosignature of the vesicle can comprise one or more biomarkers including, but not limited to, amyloid β, ICAM-1 (rodent), CGRP (rodent), TIMP-1 (rodent), CLR-1 (rodent), HSP-27 (rodent), FABP (rodent), ATP5B, ATP5H, ATP6V1B, DNM1, NDUFV2, NSF, PDHB, FGF2, ALDH7A1, AGXT2L1, AQP4, PCNT2, FGFR1, FGFR2, FGFR3, AQP4, a mutation of Dysbindin, DAOA/G30, DISC1, neuregulin-1, IFITM3, SERPINA3, GLS, ALDH7A1, BASP1, OX42, ED9, apolipoprotein D (rodent), miR-7, miR-24, miR-26b, miR-29b, miR-30b, miR-30e, miR-92, miR-195, miR-181b, DISC1, dysbindin, neuregulin-1, seratonin 2a receptor, and NURR1.

Neurology: Standard of Care

Determining the biosignature of a vesicle, the amount of vesicles, or both, of a sample from a subject suffering from a neurological disorder or disease can be used to select a standard of care for the subject. An neurological disorder or disease can be treated according to symptoms associated with the condition. The standard of care can include, for example, a pharmaceutical drug. A pharmaceutical drug includes, but is not limited to aspirin, dipyridamole, naratriptan, apomorphine, donepezil, almotriptan malate, rufinamide, bromfenac, carbatrol, cenestin, tadalafil, clonazepam, entacapone, glatiramer acetate, pemoline, divalproex, difluprednate, zolpidem tartrate, rivastigmine tartrate, dexmethylphenidate, frovatriptan succinate, zinc acetate, sumatriptan, paliperidone, iontocaine, morphine, levetiracetam, lamotrigine, vardenafil, lidocaine, eszopiclone, fospropofol disodium, pregabalin, rizatriptan benzoate, meropenem, Methylphenidate, dihydroergotamine mesylate, Pramipexole, rimabotulinumtoxin B, naltrexone, memantine, rotigotine, gabapentin), hydrocodone, mitoxantrone, armodafinil, oxycodone, pramipexole, samarium 153 lexidronam, interferon beta-1a, dexfenfluramine, eletriptan hydrobromide, galantamine hydrobromide, ropinirole hydrochloride, riluzole, ramelteon, eldepryl, valproic acid, atomoxetine, tolcapone, carbamazepine, topiramate, oxcarbazepine, natalizumab, acetaminophen, tramadol, midazolam, lacosamide, iodixanol, lisdexamfetamine dimesylate, tetrabenazine, sodium oxybate, tizanidine hydrochloride, zolmitriptan, and zonisamide.

Other treatments that can be selected based on a vesicle profile of a subject includes those listed in Table 17, for a subject with Multiple Sclerosis; Table 19, for a subject with Parkinson's Disease; or Table 20, for a subject with depression.

TABLE 19 Classes of Drugs for Treatment of Parkinson's Disease Class Mechanism of Action Examples Dopamine Precursors Act as precursors in the synthesis of dopamine, the Levodopa, neurotransmitter that is depleted in Parkinson's Disease. Usually Levodopa- administered in combination with an inhibitor of the carboxylase carbidopa, enzyme that metabolizes levodopa. Some (e.g., Duodopa) are Levodopa- given by infusion, e.g., intraduodenal infusion benserazide, Etilevodopa, Duodopa Dopamine Agonists Mimic natural dopamine by directly stimulating striatal dopamine Bromocriptine, receptors. May be subclassed by which of the five known Cabergoline, dopamine receptor subtypes the drug activates; generally most Lisuride, Pergolide, effective are those that activate receptors the in the D2 receptor Pramipexole, family (specifically D2 and D3 receptors). Some are formulated Ropinirole, for more controlled release or transdermal delivery. Talipexole, Apomorphine, Dihydroergocryptine, Lisuride, Piribedil, Talipexole, Rotigotin CDS, Sumanirole, SLV- 308 COMT Inhibitors Inhibits COMT, the second major enzyme that metabolized Entacapone, levodopa. Tolcapone, Entacapone- Levodopa- Carbidopa fixed combination, MAO-B Inhibitors MAO-B metabolizes dopamine, and inhibitors of MAO-B thus Selegiline, prolong dopamine's half-life Rasagiline, Safinamide Antiglutamatergic Block glutamate release. Reduce levodopa-induced dyskinesia Amantadine, Agents Budipine, Talampanel, Zonisamide Anticholinergic Thought to inhibit excessive cholinergic activity that Trihexyphenidyl, Agents accompanies dopamine deficiency Benztropine, Biperiden Mixed Dopaminergic Act on several neurotransmitter systems, both dopaminergic and NS-2330, Sarizotan Agents nondopaminergic. Adenosine A2a Adenosine A2 antagonize dopamine receptors and are found in Istradefylline antagonists conjunction with dopamine receptors. Antagonists of these receptors may enhance the activity of dopamine receptors. Alpha-2 Adrenergic Not known. Yohimbine, Antagonists Idazoxan, Fipamezole Antiapoptotic Agents Can slow the death of cells associated with the neurodegenerative CEP-1347, TCH- process of Parkinson's disease. 346 Growth Factor Promote the survival and growth of dopaminergic cells. GPI-1485, Glial- Stimulators cell-line-derived Neurotrophic Factor, SR-57667, PYM-50028 Cell Replacement Replace damaged neurons with health neurons. Spheramine Therapy

TABLE 20 Classes of Drugs for Treatment of Depression Class Mechanism of Action Examples Selective Block presynaptic reuptake of serotonin. Exert little effect on Escitalopram, Sertraline, Serotonin norepinephrine or dopamine reuptake. Level of serotonin in Citalopram, Paroxetine, Reuptake the synaptic cleft is increased. Paroxetin, controlled Inhibitor (SSRI) release, Fluoxetine, Fluoxetine weekly, Fluvoxamine, olanzapine/fluoxetine combination Serotonergic/ Inhibit both serotonin reuptake and norepinephrine reuptake. Venlafaxine; Reboxetine, noradrenergic Different drugs in this class can inhibit each receptor to Milnacipran, Mirtazapine, agents different degrees. Do not affect histamine, acetylcholine, and Nefazodone, Duloxetine adrenergic receptors. Serotonergic/ Several different mechanisms. Block norepinephrine, Bupropion, Maprotiline, noradrenergic/ serotonin, and/or dopamine reuptake. Some have addictive Mianserin, Trazodone, dopaminergic agents potential due to dopamine reuptake inhibition. Dexmethylphenidate, Methyphenidate, Amineptine Tricyclic Block synaptic reuptake of serotonin and norepinephrine. Amitriptyline, Amoxapine, Antidepressants Have little effect on dopamine. Strong blockers of Clomipramine, muscarinic, histaminergic H1, and alpha-1-adrenergic Desipramine, Doxepin, receptors. Imipramine, Nortriptyline, Protriptyline, Trimipramine Irreversible Monoamine oxidase (MAO) metabolizes monoamines such as Isocarboxazid, Phenelzine, Monoamine serotonin and norepinephrine. MAO inhibitors inhibit MAO, Tranylcypromine, Oxidase thus increasing levels of serotonin and norepinephrine. Transdermal Selegiline Inhibitors Reversible See above. Short acting, reversible inhibitor, inhibits Moclobemide Monoamine deamination of serotonin, norepinephrine, and dopamine. Oxidase Inhibitors Serotonergic/ Act to block all of serotonin, norepinephrine, and dopamine DOV-216303, DOV-21947 noradenergic/ reuptake. May have addictive potential due to dopamine dopaminergic reuptake inhibition. reuptake inhibitors Noradrenergic/ Block reuptake of norepinephrine and dopamine GW-353162 dopaminergic agents Serotonin Selective antagonist of one serotonin receptor (the 5-HT₁ Agomelatine Antagonists receptor) Serotonin Partial agonist of the 5-HT_(1A) receptor. Eptapirone, Vilazodone, Agonists OPC-14523, MKC-242, Gepirone ER Substance P Modify levels of substance P, which is released during acute Aprepitant, TAK-637, CP- Antagonists stress. 122721, E6006, R-763OPC- GW-597599 Beta₃ Indirectly inhibit norepinephrine reuptake. Also being SR-58611 Adrenoreceptor investigated for treatment of obesity and diabetes because Agonists they stimulate lipolysis and thermogenesis.

In one embodiment, a treatment can be selected for a subject suffering from Alzheimer's disease. One or more biomarkers, such as, but not limited to, beta-amyloid protein, amyloid precursor protein (APP), APP670/671, APP693, APP692, APP715, APP716, APP717, APP723, presenilin 1, presenilin 2, cerebrospinal fluid amyloid beta protein 42 (CSF-Abeta42), cerebrospinal fluid amyloid beta protein 40 (CSF-Abeta40), F2 isoprostane, 4-hydroxynonenal, F4 neuroprostane, and acrolein, can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, Donepezil, Galantamine, Memantine, Rivastigmine, Tacrine, or a combination thereof.

In another embodiment, a treatment can be selected for a subject suffering from Parkinson's Disease. One or more biomarkers, such as, but not limited to, alpha synuclein, PARK7 (DJ-1), S-phase kinase-associated protein 1A (p19A/SKP1A), Heat shock protein 70 kDa, AMP-regulated phosphoprotein (ARPP-21), vesicular monoamine member 2 (VMAT2), alcohol dehydrogenase 5 (ADH5), aldehyde dehydrogenase 1A1 (ALDH1A1), egle nine homolog 1(EGLN1), proline hydroxylase 2 (PHD2), and hypoxia inducible factor (HIF), can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment, such as, but not limited to, those listed in Table 19.

In another embodiment, a treatment can be selected for a subject suffering from Parkinson's Disease. One or more biomarkers, such as, but not limited to, CRP, TNF, IL-6, S100B, and MMP can be assessed from a vesicle from the subject. Based on one or more characteristics of the one or more biomarkers, the subject can be determined to be a responder or non-responder for a treatment.

Thus, a treatment can be selected for the subject suffering from a neurology-related condition or neurological condition or disease, based on the biosignature of the subject's vesicle.

Biosignature Discovery

The systems and methods provided herein can be used in identifying a novel biosignature of a vesicle, such as one or more novel biomarkers for the diagnosis, prognosis or theranosis of a phenotype. In one embodiment, one or more vesicles can be isolated from a subject with a phenotype and a biosignature of the one or more vesicles determined. The biosignature can be compared to a subject without the phenotype. Differences between the two biosignatures can be determined and used to form a novel biosignature. The novel biosignature can then be used for identifying another subject as having the phenotype or not having the phenotype.

Differences between the biosignature from a subject with a particular phenotype can be compared to the biosignature from a subject without the particular phenotype. The one or more differences can be a difference in any characteristic of the vesicle. For example, the level or amount of vesicles in the sample, the half-life of the vesicle, the circulating half-life of the vesicle, the metabolic half-life of the vesicle, or the activity of the vesicle, or any combination thereof, can differ between the biosignature from the subject with a particular phenotype and the biosignature from the subject without the particular phenotype.

In some embodiments, one or more biomarkers differ between the biosignature from the subject with a particular phenotype and the biosignature from the subject without the particular phenotype. For example, the expression level, presence, absence, mutation, variant, copy number variation, truncation, duplication, modification, molecular association of one or more biomarkers, or any combination thereof, may differ between the biosignature from the subject with a particular phenotype and the biosignature from the subject without the particular phenotype. The biomarker can be any biomarker disclosed herein or that can be used to characterize a biological entity, including a circulating biomarker, such as protein or microRNA, a vesicle, or a component present in a vesicle or on the vesicle, such as any nucleic acid (e.g. RNA or DNA), protein, peptide, polypeptide, antigen, lipid, carbohydrate, or proteoglycan.

In an aspect, the invention provides a method of discovering a novel biosignature comprising comparing the biomarkers between two or more sample groups to identify biomarkers that show a difference between the sample groups. Multiple markers can be assessed in a panel format to potentially improve the performance of individual markers. In some embodiments, the multiple markers are assessed in a multiplex fashion. The ability of the individual markers and groups of markers to distinguish the groups can be assessed using statistical discriminate analysis or classification methods as used herein. Optimal panels of markers can be used as a biosignature to characterize the phenotype under analysis, such as to provide a diagnosis, prognosis or theranosis of a disease or condition. Optimization can be based on various criteria, including without limitation maximizing ROC AUC, accuracy, sensitivity at a certain specificity, or specificity at a certain sensitivity. The panels can include biomarkers from multiple types. For example, the biosignature can comprise vesicle antigens useful for capturing a vesicle population of interest, and the biosignature can further comprise payload markers within the vesicle population, including without limitation microRNAs, mRNAs, or soluble proteins. Optimal combinations can be identified as those vesicle antigens and payload markers with the greatest ROC AUC value when comparing two settings. As another example, the biosignature can be determined by assessing a vesicle population in addition to assessing circulating biomarkers that are not obtained by isolating exosomes, such as circulating proteins and/or microRNAs.

The phenotype can be any of those listed herein, e.g., in the “Phenotype” section above. For example, the phenotype can be a proliferative disorder such as a cancer or non-malignant growth, a perinatal or pregnancy related condition, an infectious disease, a neurological disorder, a cardiovascular disease, an inflammatory disease, an immune disease, or an autoimmune disease. The cancer includes without limitation lung cancer, non-small cell lung cancerm small cell lung cancer (including small cell carcinoma (oat cell cancer), mixed small cell/large cell carcinoma, and combined small cell carcinoma), colon cancer, breast cancer, prostate cancer, liver cancer, pancreatic cancer, brain cancer, kidney cancer, ovarian cancer, stomach cancer, melanoma, bone cancer, gastric cancer, breast cancer, glioma, gliobastoma, hepatocellular carcinoma, papillary renal carcinoma, head and neck squamous cell carcinoma, leukemia, lymphoma, myeloma, or other solid tumors.

Any of the types of biomarkers or specific biomarkers described herein can be assessed to discover a novel biosignature. In an embodiment, the biomarkers selected for discovery comprise cell-specific biomarkers as listed herein, including without limitation the genes and microRNA listed in any of FIGS. 1-60, or Tables 3-10, 12-17, 19-20, 22, 26, 28-50, 52, 54, 55-64, 66, 67, 69-71, 73-85, 89-92, and a combination thereof. For example, the biomarkers can comprise one or more marker in Table 5. The biomarkers can comprise one or more biomarker in any of Tables 6-9. The biomarkers can comprise one or more biomarker in any of the Examples herein. The biomarkers can comprise one or more treatment associated target such as a ABCC1, ABCG2, ACE2, ADA, ADH1C, ADH4, AGT, AR, AREG, ASNS, BCL2, BCRP, BDCA1, beta III tubulin, BIRC5, B-RAF, BRCA1, BRCA2, CA2, caveolin, CD20, CD25, CD33, CD52, CDA, CDKN2A, CDKN1A, CDKN1B, CDK2, CDW52, CES2, CK 14, CK 17, CK 5/6, c-KIT, c-Met, c-Myc, COX-2, Cyclin D1, DCK, DHFR, DNMT1, DNMT3A, DNMT3B, E-Cadherin, ECGF1, EGFR, EML4-ALK fusion, EPHA2, Epiregulin, ER, ERBR2, ERCC1, ERCC3, EREG, ESR1, FLT1, folate receptor, FOLR1, FOLR2, FSHB, FSHPRH1, FSHR, FYN, GART, GNRH1, GNRHR1, GSTP1, HCK, HDAC1, hENT-1, Her2/Neu, HGF, HIF1A, HIG1, HSP90, HSP90AA1, HSPCA, IGF-1R, IGFRBP, IGFRBP3, IGFRBP4, IGFRBP5, IL13RA1, IL2RA, KDR, Ki67, KIT, K-RAS, LCK, LTB, Lymphotoxin Beta Receptor, LYN, MET, MGMT, MLH1, MMR, MRP1, MS4A1, MSH2, MSH5, Myc, NFKB1, NFKB2, NFKBIA, ODC1, OGFR, p16, p21, p27, p53, p95, PARP-1, PDGFC, PDGFR, PDGFRA, PDGFRB, PGP, PGR, PI3K, POLA, POLA1, PPARG, PPARGC1, PR, PTEN, PTGS2, RAF1, RARA, RRM1, RRM2, RRM2B, RXRB, RXRG, SIK2, SPARC, SRC, SSTR1, SSTR2, SSTR3, SSTR4, SSTR5, Survivin, TK1, TLE3, TNF, TOP1, TOP2A, TOP2B, TS, TXN, TXNRD1, TYMS, VDR, VEGF, VEGFA, VEGFC, VHL, YES1, and ZAP70. The treatment-associated biomarker can be one or more marker in any of Tables 10 or 11-13. The biomarkers can comprise one or more general vesicle marker, one or more cell-specific vesicle marker, and/or one or more disease-specific vesicle marker.

The biomarkers used for biosignature discovery can comprise include markers commonly associated with vesicles, including without limitation one or more of HSPA8, CD63, Actb, GAPDH, CD9, CD81, ANXA2, HSP90AA1, ENO1, YWHAZ, PDCD6IP, CFL1, SDCBP, PKN2, MSN, MFGE8, EZR, YWHAG, PGK1, EEF1A1, PPIA, GLC1F, GK, ANXA6, ANXA1, ALDOA, ACTG1, TPI1, LAMP2, HSP90AB1, DPP4, YWHAB, TSG101, PFN1, LDHB, HSPA1B, HSPA1A, GSTP1, GNAI2, GDI2, CLTC, ANXA5, YWHAQ, TUBA1A, THBS1, PRDX1, LDHA, LAMP1, CLU, and CD86. The biomarkers can further comprise one or more of CD63, GAPDH, CD9, CD81, ANXA2, ENO1, SDCBP, MSN, MFGE8, EZR, GK, ANXA1, LAMP2, DPP4, TSG101, HSPA1A, GDI2, CLTC, LAMP1, Cd86, ANPEP, TFRC, SLC3A2, RDX, RAP1B, RAB5C, RAB5B, MYH9, ICAM1, FN1, RAB11B, PIGR, LGALS3, ITGB1, EHD1, CLIC1, ATP1A1, ARF1, RAP1A, P4HB, MUC1, KRT10, HLA-A, FLOT1, CD59, C1orf58, BASP1, TACSTD1, and STOM. Other biomarkers can be selected from those disclosed in the ExoCarta database, available at exocarta.ludwig.edu.au, which discloses proteins and RNA molecules identified in exosomes. See also Mathivanan and Simpson, ExoCarta: A compendium of exosomal proteins and RNA. Proteomics. 2009 Nov. 9(21):4997-5000.

The biomarkers used for biosignature discovery can comprise include markers commonly associated with vesicles, including without limitation one or more of A33, a33 n15, AFP, ALA, ALIX, ALP, AnnexinV, APC, ASCA, ASPH (246-260), ASPH (666-680), ASPH (A-10), ASPH (D01P), ASPH (D03), ASPH (G-20), ASPH(H-300), AURKA, AURKB, B7H3, B7H4, BCA-225, BCNP1, BDNF, BRCA, CA125 (MUC16), CA-19-9, C-Bir, CD1.1, CD10, CD174 (Lewis y), CD24, CD44, CD46, CD59 (MEM-43), CD63, CD66e CEA, CD73, CD81, CD9, CDA, CDAC11a2, CEA, C-Erb2, C-erbB2, CRMP-2, CRP, CXCL12, CYFRA21-1, DLL4, DR3, EGFR, Epcam, EphA2, EphA2 (H-77), ER, ErbB4, EZH2, FASL, FRT, FRT c.f23, GDF15, GPCR, GPR30, Gro-alpha, HAP, HBD 1, HBD2, HER 3 (ErbB3), HSP, HSP70, hVEGFR2, iC3b, IL 6 Unc, IL-1B, 1L6 Unc, IL6R, IL8, IL-8, INSIG-2, KLK2, L1CAM, LAMN, LDH, MACC-1, MAPK4, MART-1, MCP-1, M-CSF, MFG-E8, MIC1, MIF, MIS RII, MMG, MMP26, MMP7, MMP9, MS4A1, MUC1, MUC1 seq1, MUC1 seq11A, MUC17, MUC2, Ncam, NGAL, NPGP/NPFF2, OPG, OPN, p53, p53, PA2G4, PBP, PCSA, PDGFRB, PGP9.5, PIM1, PR (B), PRL, PSA, PSMA, PSME3, PTEN, R5-CD9 Tube 1, Reg IV, RUNX2, SCRN1, seprase, SERPINB3, SPARC, SPB, SPDEF, SRVN, STAT 3, STEAP1, TF (FL-295), TFF3, TGM2, TIMP-1, TIMP1, TIMP2, TMEM211, TMPRSS2, TNF-alpha, Trail-R2, Trail-R4, TrKB, TROP2, Tsg 101, TWEAK, UNC93A, VEGF A, and YPSMA-1. The biomarkers can include one or more of NSE, TRIM29, CD63, CD151, ASPH, LAMP2, TSPAN1, SNAIL, CD45, CKS1, NSE, FSHR, OPN, FTH1, PGP9, ANNEXIN 1, SPD, CD81, EPCAM, PTH1R, CEA, CYTO 7, CCL2, SPA, KRAS, TWIST1, AURKB, MMP9, P27, MMP1, HLA, HIF, CEACAM, CENPH, BTUB, INTG b4, EGFR, NACC1, CYTO 18, NAP2, CYTO 19, ANNEXIN V, TGM2, ERB2, BRCA1, B7H3, SFTPC, PNT, NCAM, MS4A1, P53, INGA3, MUC2, SPA, OPN, CD63, CD9, MUC1, UNCR3, PAN ADH, HCG, TIMP, PSMA, GPCR, RACK1, PCSA, VEGF, BMP2, CD81, CRP, PRO GRP, B7H3, MUC1, M2PK, CD9, PCSA, and PSMA. The biomarkers can also include one or more of TFF3, MS4A1, EphA2, GAL3, EGFR, N-gal, PCSA, CD63, MUC1, TGM2, CD81, DR3, MACC-1, TrKB, CD24, TIMP-1, A33, CD66 CEA, PRL, MMP9, MMP7, TMEM211, SCRN1, TROP2, TWEAK, CDACC1, UNC93A, APC, C-Erb, CD10, BDNF, FRT, GPR30, P53, SPR, OPN, MUC2, GRO-1, tsg 101 and GDF15. In embodiments, the biomarkers used to discover a biosignature comprise one or more of those shown in FIGS. 99, 100, 108A-C, 114A, and/or 115A-E.

One of skill will appreciate that any marker disclosed herein or that can be compared between two samples or sample groups of interest can be used to discover a novel biosignature for any given biological setting that can be compared.

The one or more differences can then be used to form a novel biosignature for the particular phenotype, such as the diagnosis of a condition, diagnosis of a stage of a disease or condition, prognosis of a condition, or theranosis of a condition. The novel biosignature can then be used to identify the phenotype in other subjects. The biosignature of a vesicle for a new subject can be determined and compared to the novel signature to determine if the subject has the particular phenotype for which the novel biosignature was identified from.

For example, the biosignature of a subject with cancer can be compared to another subject without cancer. Any differences can be used to form a novel biosignature for the diagnosis of the cancer. In another embodiment, the biosignature of a subject with an advanced stage of cancer can be compared to another subject with a less advanced stage of cancer. Any differences can be used to form a novel biosignature for the classification of the stage of cancer. In yet another embodiment, the biosignature of a subject with an advanced stage of cancer can be compared to another subject with a less advanced stage of cancer. Any differences can be used to form a novel biosignature for the classification of the stage of cancer.

In one embodiment, the phenotype is drug resistance or non-responsiveness to a therapeutic. One or more vesicles can be isolated from a non-responder to a particular treatment and the biosignature of the vesicle determined. The biosignature of the vesicle obtained from the non-responsder can be compared to the biosignature of a vesicle obtained from a responsder. Differences between the biosignature from the non-responder can be compared to the biosignature from the responder. The one or more differences can be a difference in any characteristic of the vesicle. For example, the level or amount of vesicles in the sample, the half-life of the vesicle, the circulating half-life of the vesicle, the metabolic half-life of the vesicle, the activity of the vesicle, or any combination thereof, can differ between the biosignature from the non-responder and the biosignature from the responder.

In some embodiments, one or more biomarkers differ between the biosignature from the non-responder and the biosignature from the responder. For example, the expression level, presence, absence, mutation, variant, copy number variation, truncation, duplication, modification, molecular association of one or more biomarkers, or any combination thereof, may differ between the biosignature from the non-responder and the biosignature from the responder.

In some embodiments, the difference can be in the amount of drug or drug metabolite present in the vesicle. Both the responder and non-responder can be treated with a therapeutic. A comparison between the biosignature from the responder and the biosignature from the non-responder can be performed, the amount of drug or drug metabolite present in the vesicle from the responder differs from the amount of drug or drug metabolite present in the non-responder. The difference can also be in the half-life of the drug or drug metabolite. A difference in the amount or half-life of the drug or drug metabolite can be used to form a novel biosignature for identifying non-responders and responders.

A vesicle useful for methods and compositions described herein can be discovered by taking advantage of its physicochemical characteristics. For example, a vesicle can be discovered by its size, e.g., by filtering biological matter in a known range from 30-120 nm in diameter. Size-based discovery methods, such as differential centrifugation, sucrose gradient centrifugation, or filtration have been used for isolation of a vesicle.

A vesicle can be discovered by its molecular components. Molecular property-based discovery methods include, but are not limited to, immunological isolation using antibodies recognizing molecules associated with vesicle. For example, a surface molecule associated with a vesicle includes, but not limited to, a MHC-II molecule, CD63, CD81, LAMP-1, Rab7 or Rab5.

Various techniques known in the art are applicable for validation and characterization of a vesicle. Techniques useful for validation and characterization of a vesicle includes, but is not limited to, western blot, electron microscopy, immunohistochemistry, immunoelectron microscopy, FACS (Fluorescent activated cell sorting), electrophoresis (1 dimension, 2 dimension), liquid chromatography, mass spectrometry, MALDI-TOF (matrix assisted laser desorption/ionization-time of flight), ELISA, LC-MS-MS, and nESI (nanoelectrospray ionization). For example U.S. Pat. No. 2009/0148460 describes use of an ELISA method to characterize a vesicle. U.S. Pat. No. 2009/0258379 describes isolation of membrane vesicles from biological fluids.

Vesicles can be further analyzed for one or more nucleic acids, lipids, proteins or polypeptides, such as surface proteins or peptides, or proteins or peptides within a vesicle. Candidate peptides can be identified by various techniques including mass spectrometry coupled with purification methods such as liquid chromatography. A peptide can then be isolated and its sequence can be identified by sequencing. A computer program that predicts a sequence based on exact mass of a peptide can also be used to reveal the sequence of a peptide isolated from a vesicle. For example, LTQ-Orbitrap mass spectrometry can be used for high sensitivity and high accuracy peptide sequencing. LTQ-Orbitrap method has been described (Simpson et al, Expert Rev. Proteomics 6:267-283, 2009), which is incorporated herein by reference in its entirety.

A novel vesicle biomarker can be identified using one or more methods as disclosed in PCT Publication No. WO2008138578, which is herein incorporated by reference in its entirety. In one embodiment, one or more biomarkers of a membrane vesicle are identified as a novel biomarker for selecting a treatment. A membrane vesicle obtained from a sample in the presence of a treatment can be compared to a membrane vesicle obtained from a sample in the absence of such treatment. One or more characteristics of the sample that indicates sensitivity, or resistance, to a treatment can be assessed (e.g. if the sample comprises cells, cellular characteristics such as apoptosis, cell division, expression of a gene, or the like can be determined; alternatively if the sample is from a subject, the subject's condition or symptoms can be assessed). One or more of the characteristics of the sample can be correlated to the expression level of a biomarker in a membrane vesicle to obtain a correlation coefficient. A median correlation coefficient can be calculated for the biomarker and the biomarker identified for use in determining the sensitivity of a subject to the treatment if the median correlation coefficient exceeds about 0.3. In some embodiments, the correlation coefficient exceeds 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, or 0.99 or more.

In one embodiment, one or more cell lines, such as from lung, colon, breast, ovarian, leukemia, renal, melanoma, prostate, and brain cancers can be used to identify novel biomarkers for characterizing a cancer phenotype. For example, one or more of the following cancer cell lines: NSCLC_NCIH23, NSCLC_NCIH522, NSCLC_A549ATCC, NSCLC_EKVX, NSCLC_NCIH226, NSCLC_NCIH332M, NSCLC H460, NSCLC_HOP62, NSCLC_HOP92, COLON_HT29, COLON_HCC-2998, COLON_HCT1 16, COLON_SW620, COLON_COLO205, COLON_HCT15, COLON_KM 12, BREAST MCF7, BREAST_MCF7ADRr, BREAST MDAMB231, BREAST HS578T, BREAST MDAMB435, BREAST_MDN, BREAST BT549, BREAST_T47D, OVAR_OVCAR3, OVAR_OVCAR4, OVAR_OVCAR5, OVAR_OVCAR8, OVARJGROV 1, OVAR_SKOV3, LEUK_CCRFCEM, LEUK_K562, LEUK_MOLT4, LEUK_HL60, LEUK RPMI8266, LEUK SR, RENAL_UO31, RENAL_SN12C, RENAL_A498, RENAL_CAKI1, RENAL RXF393, RENAL_(—)7860, RENAL ACHN, RENAL_TK10, MELAN LOXIMVI, MELAN_MALME3M, MELAN_SKMEL2, MELAN_SKMEL5, MELAN_SKMEL28, MELAN_M14, MELANJJ ACC62, MELAN JJ ACC257, PROSTATE_PC3, PROSTATE_DU145, CNS_SNB19, CNS_SNB75, CNS_U251, CNS SF268, CNS_SF295, and CNS_SF539, can be used to identify one or more biomarkers that vary from a non-cancer cell line, and used to characterize a cancer, such as provide a diagnosis for a cancer. In another embodiment, the one or more biomarkers identified can be used to select at treatment for a subject.

In another embodiment, a novel biosignature is determined by assessing the presence or levels of one or more biomarkers including, but not limited to, CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, and/or TNFR. A difference in the presence or level of a biomarker in a sample with a particular phenotype (e.g., a sample from a subject with cancer) as compared to another sample without the phenotype (e.g., a sample from a subject without cancer) may indicate that the biomarker is a useful component of a novel biosignature.

In one embodiment, one or more biomarkers of a membrane vesicle can be assessed (e.g., by detection of the expression of biomarker) from different cell types (e.g. membrane vesicles shed from different cell types) and measurements of the growth of those cell types in the presence of a treatment for cancer relative to the growth of the cell types in the absence of the treatment for cancer can be determined. The one or more assessments of the biomarker (such as the measurement of the expression level of biomarker) of a vesicle can be correlated with the growth of the cells to obtain a correlation coefficient. A correlation coefficient can be selected and calculated for the biomarker. The biomarker can be identified as one to use in determining the sensitivity of a subject to the particular treatment for cancer if the median correlation coefficient exceeds a threshold, e.g., about 0.3. In one embodiment, the biomarker is selected for use in selecting a treatment if the correlation coefficient exceeds about 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, or 0.99 or more. In an embodiment, the method is performed in the presence of a second treatment.

One of skill will appreciate that any of the biomarkers disclosed herein can be used as part of a biosignature for characterizing a phenotype. For example, a subset of biomarkers disclosed for biosignature discovery can be assessed in order to characterize a phenotype. The subset can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100 or more of the biomarkers.

Nucleic Acids

Vesicles can be further analyzed for one or more nucleic acids therein.

Proteins, Peptides

Vesicles can be further analyzed for proteins or polypeptides, such as surface proteins or peptides, or proteins or peptides within a vesicle. Candidate peptides can be identified by various techniques including mass spectrometry coupled with purification methods such as liquid chromatography. A peptide can then be isolated and its sequence can be identified by sequencing. A computer program that predicts a sequence based on exact mass of a peptide can also be used to reveal the sequence of a peptide isolated from a vesicle. For example, LTQ-Orbitrap mass spectrometry can be used for high sensitivity and high accuracy peptide sequencing. LTQ-Orbitrap method has been described (Simpson et al, Expert Rev. Proteomics 6:267-283, 2009), which is incorporated herein by reference in its entirety.

Lipids

Vesicles lipids can be further analyzed using methods known to those in the art.

Vesicle Compositions

Also provided herein is an isolated vesicle with a particular biosignature. The isolated vesicle can comprise one or more biomarkers or biosignatures specific for specific cell type, or for characterizing a phenotype, such as described above. For example, the isolated vesicle can comprise one or more biomarkers, such as CD63, EpCam, CD81, CD9, PCSA, PSMA, B7H3, TNFR, MFG-E8, Rab, STEAP, 5T4, or CD59. The isolated vesicle can comprise one or more of the following biomarkers: EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP, and EGFR. In one embodiment, the vesicle is EpCam+, CK+, CD45−. The isolated vesicle can have the one or more biomarkers on its surface or within the vesicle. The isolated vesicle can also comprise one or more miRNAs, such as miR-9, miR-629, miR-141, miR-671-3p, miR-491, miR-182, miR-125a-3p, miR-324-5p, miR-148B, or miR-222. In one embodiment, the vesicle comprises one or more miRNAs, such as miR-548c-5p, miR-362-3p, miR-422a, miR-597, miR-429, miR-200a, and miR-200b. In yet another embodiment, the vesicle comprises one or more miRNAs, such as miR-92a-2*, miR-147, miR-574-5p. An isolated vesicle can comprise a biomarker such as CD66, and further comprise one or more biomarkers selected from the group consisting of: EpCam, CD63, or CD9. An isolated vesicle can also comprise a fusion gene or protein, such as TMRSSG2:ERG.

An isolated vesicle can also comprise one or more biomarkers, wherein the expression level of the one or more biomarkers is higher, lower, or the same for an isolated vesicle as compared to an isolated vesicle derived from a normal cell (ie. a cell derived from a subject without a phenotype of interest). For example, an isolated vesicle can comprise one or more biomarkers selected from the group consisting of: B7H3, PSCA, MFG-E8, Rab, STEAP, PSMA, PCSA, 5T4, miR-9, miR-629, miR-141, miR-671-3p, miR-491, miR-182, miR-125a-3p, miR-324-5p, miR-148b, and miR-222, wherein the expression level of the one or more biomarkers is higher for an isolated vesicle as compared those derived from a normal cell. The isolated vesicle can comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, or 19 of the biomarkers selected from the group. The isolated vesicle can further comprising one or more biomarkers selected from the group consisting of: EpCam, CD63, CD59, CD81, or CD9.

An isolated vesicle can comprise the biomarkers PCSA, EpCam, CD63, and CD8; the biomarkers PCSA, EpCam, B7H3 and PSMA. An isolated vesicle can comprise the biomarkers miR-9, miR-629, miR-141, miR-671-3p, miR-491, miR-182, miR-125a-3p, miR-324-5p, miR-148b, and miR-222.

A composition comprising an isolated vesicle is also provided herein. The composition can comprise one or more isolated vesicles. For example, the composition can comprise a plurality of vesicles, or one or more populations of vesicles.

The composition can be substantially enriched for vesicles. For example, the composition can be substantially absent of cellular debris, cells, or non-exosomal proteins, peptides, or nucleic acids (such as biological molecules not contained within the vesicles). The cellular debris, cells, or non-exosomal proteins, peptides, or nucleic acids, can be present in a biological sample along with vesicles. A composition can be substantially absent of cellular debris, cells, or non-exosomal proteins, peptides, or nucleic acids (such as biological molecules not contained within the vesicles), can be obtained by any method disclosed herein, such as through the use of one or more binding agents or capture agents for one or more vesicles. The vesicles can comprise at least 30, 40, 50, 60, 70, 80, 90, 95 or 99% of the total composition, by weight or by mass. The vesicles of the composition can be a heterogeneous or homogeneous population of vesicles. For example, a homogeneous population of vesicles comprises vesicles that are homogeneous as to one or more properties or characteristics. For example, the one or more characteristics can be selected from a group consisting of: one or more of the same biomarkers, a substantially similar or identical biosignature, derived from the same cell type, vesicles of a particular size, and a combination thereof.

Thus, in some embodiments, the composition comprises a substantially enriched population of vesicles. The composition can be enriched for a population of vesicles that are at least 30, 40, 50, 60, 70, 80, 90, 95 or 99% homogeneous as to one or more properties or characteristics. For example, the one or more characteristics can be selected from a group consisting of: one or more of the same biomarkers, a substantially similar or identical biosignature, derived from the same cell type, vesicles of a particular size, and a combination thereof. For example, the population of vesicles can be homogeneous by all having a particular biosignature, having the same biomarker, having the same biomarker combination, or derived from the same cell type. In some embodiments, the composition comprises a substantially homogeneous population of vesicles, such as a population with a specific biosignature, derived from a specific cell, or both.

The population of vesicles can comprise one or more of the same biomarkers. The biomarker can be any component such as any nucleic acid (e.g. RNA or DNA), protein, peptide, polypeptide, antigen, lipid, carbohydrate, or proteoglycan. For example, each vesicle in a population can comprise the same or identical one or more biomarkers. In some embodiments, each vesicle comprises the same 1, 2, 3, 4, 5, 6, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75 or 100 biomarkers. The one or more biomarkers can be selected from FIGS. 1, 3-60.

The vesicle population comprising the same or identical biomarker can refer to each vesicle in the population having the same presence or absence, expression level, mutational state, or modification of the biomarker. For example, an enriched population of vesicle can comprise vesicles wherein each vesicle has the same biomarker present, the same biomarker absent, the same expression level of a biomarker, the same modification of a biomarker, or the same mutation of a biomarker. The same expression level of a biomarker can refer to a quantitative or qualitative measurement, such as the vesicles in the population underexpress, overexpress, or have the same expression level of a biomarker as compared to a reference level.

Alternatively, the same expression level of a biomarker can be a numerical value representing the expression of a biomarker that is similar for each vesicle in a population. For example the copy number of a miRNA, the amount of protein, or the level of mRNA of each vesicle, can be quantitatively similar for each vesicle in a population, such that the numerical amount of each vesicle is ±1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, or 20% from the amount in each other vesicle in the population, as such variations are appropriate.

In some embodiments, the composition comprises a substantially enriched population of vesicles, wherein the vesicles in the enriched population has a substantially similar or identical biosignature. The biosignature can comprise one or more characteristic of the vesicle, such as the level or amount of vesicles, temporal evaluation of the variation in vesicle half-life, circulating vesicle half-life, metabolic half-life of a vesicle, or the activity of a vesicle. The biosignature can also comprise the presence or absence, expression level, mutational state, or modification of a biomarker, such as those described herein.

The biosignature of each vesicle in the population can be at least 30, 40, 50, 60, 70, 80, 90, 95, or 99% identical. In some embodiments, the biosignature of each vesicle is 100% identical. The biosignature of each vesicle in the enriched population can have the same 1, 2, 3, 4, 5, 6, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 50, 75 or 100 characteristics. For example, a biosignature of a vesicle in an enriched population can be the presence of a first biomarker, the presence of a second biomarker, and the underexpression of a third biomarker. Another vesicle in the same population can be 100% identical, having the same first and second biomarkers present and underexpression of the third biomarker. Alternatively, a vesicle in the same population can have the same first and second biomarkers, but not have underexpression of the third biomarker.

In some embodiments, the composition comprises a substantially enriched population of vesicles, wherein the vesicles are derived from the same cell type. For example, the vesicles can all be derived from cells of a specific tissue, cells from a specific tumor of interest or a diseased tissue of interest, circulating tumor cells, or cells of maternal or fetal origin. The vesicles can all be derived from tumor cells. The vesicles can all be derived from lung, pancreas, stomach, intestine, bladder, kidney, ovary, testis, skin, colorectal, breast, prostate, brain, esophagus, liver, placenta, or fetal cells.

The composition comprising a substantially enriched population of vesicles can also comprise vesicles are of a particular size. For example, the vesicles can all a diameter of greater than about 10, 20, or nm. They can all have a diameter of about 30-1000 nm, about 30-800 nm, about 30-200 nm, or about 30-100 nm. In some embodiments, the vesicles can all have a diameter of less than about 10,000 nm, 1000 nm, 800 nm, 500 nm, 200 nm, 100 nm or 50 nm.

The population of vesicles homogeneous for one or more characteristics can comprises at least about 30, 40, 50, 60, 70, 80, 90, 95, or 99% of the total vesicle population of the composition. In some embodiments, a composition comprising a substantially enriched population of vesicles comprises at least 2, 3, 4, 5, 10, 20, 25, 50, 100, 250, 500, or 1000 times the concentration of vesicle as compared to a concentration of the vesicle in a biological sample from which the composition was derived. In yet other embodiments, the composition can further comprise a second enriched population of vesicles, wherein the population of vesicles is at least 30% homogeneous as to one or more characteristics, as described herein.

Multiplex analysis can be used to obtain a composition substantially enriched for more than one population of vesicles, such as at least 2, 3, 4, 5, 6, 7, 8, 9, 10 vesicle, populations. Each substantially enriched vesicle population can comprise at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 46, 47, 48, or 49% of the composition, by weight or by mass. In some embodiments, the substantially enriched vesicle population comprises at least about 30, 40, 50, 60, 70, 80, 90, 95, or 99% of the composition, by weight or by mass.

A substantially enriched population of vesicles can be obtained by using one or more methods, processes, or systems as disclosed herein. For example, isolation of a population of vesicles from a sample can be performed by using one or more binding agents for one or more biomarkers of a vesicle, such as using two or more binding agents that target two or more biomarkers of a vesicle. One or more capture agents can be used to obtain a substantially enriched population of vesicles. One or more detection agents can be used to identify a substantially enriched population of vesicles.

In one embodiment, a population of vesicles with a particular biosignature is obtained by using one or more binding agents for the biomarkers of the biosignature. The vesicles can be isolated resulting in a composition comprising a substantially enriched population of vesicles with the particular biosignature. In another embodiment, a population of vesicles with a particular biosignature of interest can be obtained by using one or more binding agents for biomarkers that are not a component of the biosignature of interest. Thus, the binding agents can be used to remove the vesicles that do not have the biosignature of interest and the resulting composition is substantially enriched for the population of vesicles with the particular biosignature of interest. The resulting composition can be substantially absent of the vesicles comprising a biomarker for the binding agent.

Detection System and Kits

Also provided is a detection system configured to determine one or more biosignatures for a vesicle. The detection system can be used to detect a heterogeneous population of vesicles or one or more homogeneous population of vesicles. The detection system can be configured to detect a plurality of vesicles, wherein at least a subset of the plurality of vesicles comprises a different biosignature from another subset of the plurality of vesicles. The detection system detect at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, or 100 different subsets of vesicles, wherein each subset of vesicles comprises a different biosignature. For example, a detection system, such as using one or more methods, processes, and compositions disclosed herein, can be used to detect at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, or 100 different populations of vesicles.

The detection system can be configured to assess at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 1000, 2500, 5000, 7500, 10,000, 100,000, 150,000, 200,000, 250,000, 300,000, 350,000, 400,000, 450,000, 500,000, 750,000, or 1,000,000 different biomarkers for one or more vesicles. In some embodiments, the one or more biomarkers are selected from FIGS. 1, 3-60, or as disclosed herein. The detection system can be configured to assess a specific population of vesicles, such as vesicles from a specific cell-of-origin, or to assess a plurality of specific populations of vesicles, wherein each population of vesicles has a specific biosignature.

The detection system can be a low density detection system or a high density detection system. For example, a low density detection system can detect up to 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 different vesicle populations, whereas a high density detection system can detect at least about 15, 20, 25, 50, or 100 different vesicle populations In another embodiment, a low density detection system can detect up to about 100, 200, 300, 400, or 500 different biomarkers, whereas a high density detection system can detect at least about 750, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9,000, 10,000, 15,000, 20,000, 25,000, 50,000, or 100,000 different biomarkers. In yet another embodiment, a low density detection system can detect up to about 100, 200, 300, 400, or 500 different biosignatures or biomarker combinations, whereas a high density detection system can detect at least about 750, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9,000, 10,000, 15,000, 20,000, 25,000, 50,000, or 100,000 biosignatures or biomarker combinations.

The detection system can comprise a probe that selectively hybridizes to a vesicle. The detection system can comprise a plurality of probes to detect a vesicle. In some embodiments, a plurality of probes is used to detect the amount of vesicles in a heterogeneous population of vesicles. In yet other embodiments, a plurality of probes is used to detect a homogeneous population of vesicles. A plurality of probes can be used to isolate or detect at least two different subsets of vesicles, wherein each subset of vesicles comprises a different biosignature.

A detection system, such as using one or more methods, processes, and compositions disclosed herein, can comprise a plurality of probes configured to detect, or isolate, such as using one or more methods, processes, and compositions disclosed herein at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, or 100 different subsets of vesicles, wherein each subset of vesicles comprises a different biosignature.

For example, a detection system can comprise a plurality of probes configured to detect at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, or 100 different populations of vesicles. The detection system can comprise a plurality of probes configured to selectively hybridize to at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 1000, 2500, 5000, 7500, 10,000, 100,000, 150,000, 200,000, 250,000, 300,000, 350,000, 400,000, 450,000, 500,000, 750,000, or 1,000,000 different biomarkers for one or more vesicles. In some embodiments, the one or more biomarkers are selected from FIGS. 1, 3-60, or as disclosed herein. The plurality of probes can be configured to assess a specific population of vesicles, such as vesicles from a specific cell-of-origin, or to assess a plurality of specific populations of vesicles, wherein each population of vesicles has a specific biosignature.

The detection system can be a low density detection system or a high density detection system comprising probes to detect vesicles. For example, a low density detection system can comprise probes to detect up to 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 different vesicle populations, whereas a high density detection system can comprise probes to detect at least about 15, 20, 25, 50, or 100 different vesicle populations. In another embodiment, a low density detection system can comprise probes to detect up to about 100, 200, 300, 400, or 500 different biomarkers, whereas a high density detection system can comprise probes to detect at least about 750, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9,000, 10,000, 15,000, 20,000, 25,000, 50,000, or 100,000 different biomarkers. In yet another embodiment, a low density detection system can comprise probes to detect up to about 100, 200, 300, 400, or 500 different biosignatures or biomarker combinations, whereas a high density detection system can comprise probes to detect at least about 750, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9,000, 10,000, 15,000, 20,000, 25,000, 50,000, or 100,000 biosignatures or biomarker combinations.

The probes can be specific for detecting a specific vesicle population, for example a vesicle with a particular biosignature, and as described above. A plurality of probes for detecting prostate specific vesicles is also provided. A plurality of probes can comprise probes for detecting one or more of the following biomarkers: CD9, PSCA, TNFR, CD63, MFG-E8, EpCAM, Rab, CD81, STEAP, PCSA, 5T4, EpCAM, PSMA, CD59, CD66, CD24 and B7H3. A plurality of probes for detecting Bcl-XL, ERCC1, Keratin 15, CD81/TAPA-1, CD9, Epithelial Specific Antigen (ESA), and Mast Cell Chymase can also be provided. In one embodiment, the plurality of probes comprises one or more probes for detecting TMEM211 and/or CD24.

The plurality of probes can also comprise one or more probes for detecting CD9, EphA2, EGFR, B7H3, PSM, PCSA, CD63, STEAP, CD81, ICAM1, A33, DR3, CD66e, MFG-E8, TROP-2, Mammaglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, EpCam, neurokinin receptor-1 (NK-1 or NK-1R), NK-2, Pai-1, CD45, CD10, HER2/ERBB2, AGTR1, NPY1R, MUC1, ESA, CD133, GPR30, BCA225, CD24, CA15.3 (MUC1 secreted), CA27.29 (MUC1 secreted), NMDAR1, NMDAR2, MAGEA, CTAG1B, NY-ESO-1, SPB, SPC, NSE, PGP9.5, P2RX7, NDUFB7, NSE, GAL3, osteopontin, CHI3L1, IC3b, mesothelin, SPA, AQP5, GPCR, hCEA-CAM, PTP IA-2, CABYR, TMEM211, ADAM28, UNC93A, MUC17, MUC2, IL10R-beta, BCMA, HVEM/TNFRSF14, Trappin-2 Elafin, ST2/IL1 R4, TNFRF14, CEACAM1, TPA1, LAMP, WF, WH1000, PECAM, BSA, TNFR, or a combination thereof.

A plurality of probes for detecting one or more miRNAs of a vesicle can comprise probes for detecting one or more of the following miRNAs: miR-9, miR-629, miR-141, miR-671-3p, miR-491, miR-182, miR-125a-3p, miR-324-5p, miR-148b, and miR-222. In another embodiment, the plurality of probes comprises one or more probes for detecting EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP, and EGFR. In some embodiments, the plurality of probes comprises one or more probes for detecting EpCam, CD9, PCSA, CD63, CD81, PSMA, and B7H3. In other embodiments, the plurality of probes comprises one or more probes for detecting EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP, and EGFR. In yet another embodiment, a subset of the plurality of probes are capture agents for one or more of EpCam, CD9, PCSA, CD63, CD81, PSMA, B7H3, PSCA, ICAM, STEAP, and EGFR, and another subset are probes for detecting one or more of CD9, CD63, and CD81. A plurality of probes can also comprises one or more probes for detecting r miR-92a-2*, miR-147, miR-574-5p, or a combination thereof. A plurality of probes can also comprise one or more probes for detecting miR-548c-5p, miR-362-3p, miR-422a, miR-597, miR-429, miR-200a, miR-200b or a combination thereof. A plurality of probes can also comprise one or more probes for detecting EpCam, CK, and CD45. In some embodiments, the one or more probes may be capture agents. In another embodiment, the probes may be detection agents. In yet another embodiment, the plurality of probes comprises capture and detection agents.

The probes, such as capture agents, may be attached to a solid substrate, such as an array or bead. Alternatively, the probes, such as detection agents, are not attached. The detection system may be an array based system, a sequencing system, a PCR-based system, or a bead-based system, such as described above. The detection system can also be a microfluidic device as described above.

The detection system may be part of a kit. Alternatively, the kit may comprise the one or more probe sets or plurality of probes, as described herein. The kit may comprise probes for detecting a vesicle or a plurality of vesicles, such as vesicles in a heterogeneous population. The kit may comprise probes for detecting a homogeneous population of vesicles. For example, the kit may comprise probes for detecting a population of specific cell-of-origin vesicles, or vesicles with the same specific biosignature.

Portfolios

Portfolios of multiplexed markers to guide clinical decisions and disease detection and management can be established such that the combination of biosignatures in the portfolio exhibit improved sensitivity and specificity relative to individual biosignatures or randomly selected combinations of biosignatures. In the context of the instant invention, the sensitivity of the portfolio can be reflected in the fold differences exhibited by a biosignature's expression in the diseased state relative to the normal state. Specificity can be reflected in statistical measurements of the correlation of the signaling of gene expression, for example, with the condition of interest (e.g. standard deviation can be a used as such a measurement). In considering a group of biosignature for inclusion in a portfolio, a small standard deviation in measurements correlates with greater specificity. Other measurements of variation such as correlation coefficients can also be used in this capacity.

When combining biomarkers or biosignatures in this invention In Vitro Diagnostic Multivariate Index Assays (IVDMIAs) guidelines and regulations may apply. IVDMIAs can apply to biosignatures as defined as a set of 2 or more markers composed of any combination of genes, gene alterations, mutations, amplifications, deletions, polymorphisms or methylations, or proteins, peptides, polypeptides or RNA molecules, miRNAs, mRNAs, snoRNAs, hnRNAs or RNA that can be grouped so that information obtained about the set of biosignatures in the group provides a sound basis for making a clinically relevant judgment such as a diagnosis, prognosis, or treatment choice. These sets of biosignatures make up various portfolios of the invention. As with most diagnostic markers, it is often desirable to use the fewest number of markers sufficient to make a correct medical judgment. This prevents a delay in treatment pending further analysis as well inappropriate use of time and resources. Preferably, portfolios are established such that the combination of biosignatures in the portfolio exhibit improved sensitivity and specificity relative to individual biosignatures or randomly selected combinations of biosignatures. In the context of the instant invention, the sensitivity of the portfolio can be reflected in the fold differences exhibited by a biosignature's expression in the diseased state relative to the normal state. Specificity can be reflected in statistical measurements of the correlation of the signaling of gene expression, for example, with the condition of interest. In considering a group of markers in a biosignature for inclusion in a portfolio, standard deviations, variances, co-variances, correlation coefficients, weighted averages, arithmetic sums, means, multiplicative values, weighted or balanced values or any mathematical manipulation of the values of 2 or more markers that can together be used to calculate a value or score that taken as a whole can be shown to produce greater sensitivity, specificity, negative predictive value, positive predictive value or accuracy can also be used in this capacity and are within the scope of this invention.

In another embodiment pattern recognition methods can be used. One example involves comparing biomarker expression profiles for various biomarkers (or biosignature portfolios) to ascribe diagnoses. The expression profiles of each of the biomarker comprising the biosignature portfolio are fixed in a medium such as a computer readable medium.

In one example, a table can be established into which the range of signals (e.g., intensity measurements) indicative of disease or physiological state is input. Actual patient data can then be compared to the values in the table to determine whether the patient samples are normal, benign, diseased, or represent a specific physiological state. In a more sophisticated embodiment, patterns of the expression signals (e.g., fluorescent intensity) are recorded digitally or graphically. In the example of RNA expression patterns from the biomarker portfolios used in conjunction with patient samples are then compared to the expression patterns. Pattern comparison software can then be used to determine whether the patient samples have a pattern indicative of the disease, a given prognosis, a pattern that indicates likeliness to respond to therapy, or a pattern that is indicative of a particular physiological state. The expression profiles of the samples are then compared to the portfolio of a control cell. If the sample expression patterns are consistent with the expression pattern(s) for disease, prognosis, or therapy-related response then (in the absence of countervailing medical considerations) the patient is diagnosed as meeting the conditions that relate to these various circumstances. If the sample expression patterns are consistent with the expression pattern derived from the normal/control vesicle population then the patient is diagnosed negative for these conditions.

In another exemplary embodiment, a method for establishing biomarker expression portfolios is through the use of optimization algorithms such as the mean variance algorithm widely used in establishing stock portfolios. This method is described in detail in the U.S. Application Publication No. 20030194734, incorporated herein by reference. Alternatively, measured DNA alterations, changes in mRNA, protein, or metabolites to phenotypic readouts of efficacy and toxicity may be modeled and analyzed using algorithms, systems and methods described in U.S. Pat. Nos. 7,089,168, 7,415,359 and U.S. Application Publication Nos. 20080208784, 20040243354, or 20040088116, each of which is herein incorporated by reference in its entirety.

An exemplary process of biosignature portfolio selection and characterization of an unknown is summarized as follows:

(1) Choose baseline class.

(2) Calculate mean, and standard deviation of each biomarker for baseline class samples.

(3) Calculate (X*Standard Deviation+Mean) for each biomarker. This is the baseline reading from which all other samples will be compared. X is a stringency variable with higher values of X being more stringent than lower.

(4) Calculate ratio between each Experimental sample versus baseline reading calculated in step 3.

(5) Transform ratios such that ratios less than 1 are negative (eg. using Log base 10). (Under expressed biomarkers now correctly have negative values necessary for MV optimization).

(6) These transformed ratios are used as inputs in place of the asset returns that are normally used in the software application.

(7) The software will plot the efficient frontier and return an optimized portfolio at any point along the efficient frontier.

(8) Choose a desired return or variance on the efficient frontier.

(9) Calculate the Portfolio's Value for each sample by summing the multiples of each gene's intensity value by the weight generated by the portfolio selection algorithm.

(10) Calculate a boundary value by adding the mean Biosignature Portfolio Value for Baseline groups to the multiple of Y and the Standard Deviation of the Baseline's Biosignature Portfolio Values. Values greater than this boundary value shall be classified as the Experimental Class.

(11) Optionally one can reiterate this process until best prediction.

The process of selecting a biosignature portfolio can also include the application of heuristic rules. Preferably, such rules are formulated based on biology and an understanding of the technology used to produce clinical results. More preferably, they are applied to output from the optimization method. For example, the mean variance method of biosignature portfolio selection can be applied to microarray data for a number of biomarkers differentially expressed in subjects with a specific disease. Output from the method would be an optimized set of biomarkers that could include those that are expressed in vesicles as well as in diseased tissue. If samples used in the testing method are obtained from vesicles and certain biomarkers differentially expressed in instances of disease or physiological state could also be differentially expressed in vesicles, then a heuristic rule can be applied in which a biosignature portfolio is selected from the efficient frontier excluding those that are differentially expressed in vesicles. Of course, the rule can be applied prior to the formation of the efficient frontier by, for example, applying the rule during data pre-selection.

Other statistical, mathematical and computational algorithms for the analysis of linear and non-linear feature subspaces, feature extraction and signal deconvolution in large scale datasets to identify vesicle-derived multiplex analyte profiles for diagnosis, prognosis and therapy selection and/or characterization of define physiological states can be done using any combination of unsupervised analysis methods, including but not limited to: principal component analysis (PCA) and linear and non-linear independent component analysis (ICA); blind source separation, nongaussinity analysis, natural gradient maximum likelihood estimation; joint-approximate diagonalization; eigenmatrices; Gaussian radical basis function, kernel and polynominal kernel analysis sequential floating forward selection.

Computer Systems

A vesicle can be assayed for molecular features, for example, by determining an amount, presence or absence of one or more biomarkers such as listed FIGS. 1, 3-60. The data generated can be used to produce a biosignature, which can be stored and analyzed by a computer system, such as shown in FIG. 65. The assaying or correlating of the biosignature with one or more phenotypes can also be performed by computer systems, such as by using computer executable logic.

A computer system, such as shown in FIG. 65, can be used to transmit data and results following analysis. Accordingly, FIG. 65 is a block diagram showing a representative example logic device through which results from a vesicle can be analyzed and the analysis reported or generated. FIG. 65 shows a computer system (or digital device) 800 to receive and store data generated from a vesicle, analyze of the data to generate one or more biosignatures, and produce a report of the one or more biosignatures or phenotype characterization. The computer system can also perform comparisons and analyses of biosignatures generated, and transmit the results. Alternatively, the computer system can receive raw data of vesicle analysis, such as through transmission of the data over a network, and perform the analysis.

The computer system 800 may be understood as a logical apparatus that can read instructions from media 811 and/or network port 805, which can optionally be connected to server 809 having fixed media 812. The system shown in FIG. 65 includes CPU 801, disk drives 803, optional input devices such as keyboard 815 and/or mouse 816 and optional monitor 807. Data communication can be achieved through the indicated communication medium to a server 809 at a local or a remote location. The communication medium can include any means of transmitting and/or receiving data. For example, the communication medium can be a network connection, a wireless connection or an internet connection. Such a connection can provide for communication over the World Wide Web. It is envisioned that data relating to the present invention can be transmitted over such networks or connections for reception and/or review by a party 822. The receiving party 822 can be but is not limited to an individual, a health care provider or a health care manager. Thus, the information and data on a test result can be produced anywhere in the world and transmitted to a different location. For example, when an assay is conducted in a differing building, city, state, country, continent or offshore, the information and data on a test result may be generated and cast in a transmittable form as described above. The test result in a transmittable form thus can be imported into the U.S. to receiving party 822. Accordingly, the present invention also encompasses a method for producing a transmittable form of information on the diagnosis of one or more samples from an individual. The method comprises the steps of (1) determining a diagnosis, prognosis, theranosis or the like from the samples according to methods of the invention; and (2) embodying the result of the determining step into a transmittable form. The transmittable form is the product of the production method. In one embodiment, a computer-readable medium includes a medium suitable for transmission of a result of an analysis of a biological sample, such as biosignatures. The medium can include a result regarding a vesicle, such as a biosignature of a subject, wherein such a result is derived using the methods described herein.

Ex Vivo Harvesting of Vesicles

A vesicle for analysis and determination of a phenotype can also be from ex vivo harvesting. Cells can be cultured and vesicles released from cells of interest in culture either result spontaneously or can be stimulated to release vesicles into the medium. (see for example, Zitvogel, et al. 1998. Nat. Med. 4: 594-600; Chaput, et al. 2004. J. Immunol. 172: 2137-214631: 2892-2900; Escudier, et al. 2005. J Transl. Med. 3: 10; Morse, et al. 2005, J. Transl. Med. 3: 9; Peche, et al. 2006. Am. J. Transplant. 6: 1541-1550; Kim, et al. 2005. J. Immunol. 174: 6440-6448, all of which are herein incorporated by reference in their entireties). Cell lines or tissue samples can be grown to 80% confluence before being cultured in fresh DMEM for 72 h. Subsequent vesicle production can be stimulated (see, for example, heat shock treatment of melanoma cells as described by Dressel, et al. 2003. Cancer Res. 63: 8212-8220, which is herein incorporated by reference in its entirety). The supernatant can then be harvested and vesicles prepared as described herein.

A vesicle produced ex vivo can, in one example, be cultured from a cell-of-origin or cell line of interest, vesicles can be isolated from the cell culture medium and subsequently labeled with a magnetic label, a fluorescent moiety, a radioisotope, an enzyme, a chemiluminescent probe, a metal particle, a non-metal colloidal particle, a polymeric dye particle, a pigment molecule, a pigment particle, an electrochemically active species, semiconductor nanocrystal or other nanoparticles including quantum dots or gold particles to be reintroduced in vivo as a label for imaging analysis. Ex vivo cultured vesicles can alternatively be used to identify novel biosignatures by setting up culturing conditions for a given cell-of-origin with characteristics of interest, for example a culture of lung cancer cells or cell line with a known EGFR mutation that confers resistant to or susceptibility to gefitinib, then exposing the cell culture to gefitinib, isolating vesicles that arise from the culture and subsequently analyzing them on a discovery array to look for novel antigens or binding agents expressed on the outside of vesicles that could be used as a biosignature to capture this species of vesicle. Additionally, it would be possible to isolate any other biomarkers or biosignatures found within these vesicles for discovery of novel signatures (including but not limited to nucleic acids, proteins, lipids, or combinations thereof) that may have clinical diagnostic, prognostic or therapy related implication.

Cells of interest can also be first isolated and cultured from tissues of interest. For example, human hair follicles in the growing phase, anagen, can be plucked individually from a patient's scalp using sterile equipment and plasticware, taking care not to damage the follicle. Each sample can be transferred to a Petri dish containing sterile PBS for tissue culture. Isolated human anagen hair follicles can be carefully transferred to an individual well of a 24-well plate containing 1 ml of William's E medium. Follicles can be maintained free-floating at 37° C. in an atmosphere of 5% CO₂ and 95% air in a humidified incubator. Medium can be changed every 3 days, taking care not to damage the follicles. Cells can then be collected and spun down from the media. Vesicles may then be isolated using antigens or cellular binding partners that are specific to such cell-of-origin specific vesicles using methods as previously described. Biomarkers and biosignatures can then be isolated and characterized by methods known to those skilled in the art.

Cells of interest may also be cultured under microgravity or zero-gravity conditions or under a free-fall environment. For example, NASA's bioreactor technology will allow such cells to be grown at much faster rate and in much greater quantities. Vesicles may then be isolated using antigens or cellular binding partners that are specific to such cell-of-origin specific vesicles using methods as previously described.

Rotating wall vessels or RWVersus are a class of bioreactors developed by and for NASA that are designed to grow suspension cultures of cells in a quiescent environment that simulates microgravity can also be used. (see for example, U.S. Pat. Nos. 5,026,650; 5,153,131; 5,153,133; 5,437,998; 5,665,594; 5,702,941; 7,351,584, 5,523,228, 5,104,802, 6,117,674, Schwarz, R P, et al., J Tiss. Cult. Meth. 14:51-58, 1992; Martin et al., Trends in biotechnology 2004; 22; 80-86, Li et al., Biochemical Engineering Journal 2004; 18; 97-104, Ashammakhi et al., Journal Nanoscience Nanotechnology 2006; 9-10: 2693-2711, Zhang et al., International Journal of Medicine 2007; 4: 623-638, Cowger, N L, et al., Biotechnol. Bioeng 64:14-26, 1999, Spaulding, G F, et al., J. Cell. Biochem. 51:249-251, 1993, Goodwin, T J et al., Proc. Soc. Exp. Biol. Med. 202:181-192, 1993; Freed, L E et al., In Vitro Cell. Dev. Biol. 33:381-385, 1997, Clejan, S. et al, Biotechnol. Bioeng. 50:587-597, 1996). Khaoustov, V I, et al., In Vitro Cell. Dev. Biol. 35:501-509. 1999, each of which is herein incorporated by reference in its entirety).

Alternatively, cells of interest or cell-of-origin specific vesicles that have been isolated may be cultured in a stationary phase plug-flow bioreactor as generally described in U.S. Pat. No. 6,911,201, and U.S. Application Publication Nos. 20050181504, 20050180958, 20050176143 and 20050176137, each of which is herein incorporated by reference in its entirety. Alternatively, cells of interest or cell-origin specific vesicles may also be isolated and cultured as generally described in U.S. Pat. No. 5,486,359.

One embodiment can include the steps of providing a tissue specimen containing cells of interest or cell-origin specific vesicles, adding cells or vesicles from the tissue specimen to a medium which allows, when cultured, for the selective adherence of only the cells of interest or cell-origin specific vesicles to a substrate surface, culturing the specimen-medium mixture, and removing the non-adherent matter from the substrate surface is generally described in U.S. Pat. No. 5,486,359, which is herein incorporated by reference in its entirety.

EMBODIMENTS

In an aspect, the invention provides a method of detecting a biosignature comprising determining a level of one or more biomarker in a biological sample. In an embodiment, the one or more biomarker comprises a tetraspanin, e.g., CD9. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation prostate, lung, colon, breast, bladder, endometrial, liver, pancreatic, ovarian, esophageal or kidney cancer.

In another aspect, the invention provides a method of detecting a biosignature comprising determining a level of one or more biomarker in a biological sample. The one or more biomarker can be selected from the group consisting of MS4A1, PRB, DR3, and a combination thereof. The one or more biomarker can be selected from the group consisting of PRB, MACC1, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a lung cancer.

In still another aspect, the invention provides a method of detecting a biosignature comprising determining a level of one or more biomarker in a biological sample. In an embodiment, the one or more biomarker is selected from the group consisting of Gal3, BCA200, and a combination thereof. In another embodiment, the one or more biomarker is selected from the group consisting of OPN, NCAM, and a combination thereof. The one or more biomarker can be selected from the group consisting of Gal3, BCA200, OPN, NCAM, and a combination thereof. The one or more biomarker can be selected from the group consisting of Gal3 and/or BCA200, OPN and/or NCAM, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer.

In an aspect, the invention provides a biosignature comprising determining a level of one or more biomarker in a biological sample. The one or more biomarker can be selected from the group consisting of a tetraspanin, CD45, FasL, CTLA4, CD31, DLL4, VEGFR2, HIF2a, Tie2, Ang1, Muc1, CD147, TIMP1, TIMP2, MMP7, MMP9, and a combination thereof. The one or more biomarker can be selected from the group consisting of CD83 and FasL, CTLA4 and CD80, CD147 and TIMP1, TIMP2 and MMP9, HIF2a and Ang1, VEGFR2 and Tie2, CD45 and CTL4A, DLL4 and CD31, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer.

In another aspect, the invention provides a biosignature comprising determining a level of one or more biomarker in a biological sample. The one or more biomarker can be selected from the group consisting of 5T4 (trophoblast), ADAM10, AGER/RAGE, APC, APP (β-amyloid), ASPH (A-10), B7H3 (CD276), BACE1, BAI3, BRCA1, BDNF, BIRC2, C1GALT1, CA125 (MUC16), Calmodulin 1, CCL2 (MCP-1), CD9, CD10, CD127 (IL7R), CD174, CD24, CD44, CD63, CD81, CEA, CRMP-2, CXCR3, CXCR4, CXCR6, CYFRA 21, derlin 1, DLL4, DPP6, E-CAD, EpCaM, EphA2 (H-77), ER(1) ESR1α, ER(2) ESR213, Erb B4, Erbb2, erb3 (Erb-B3) PA2G4, FRT (FLT1), Gal3, GPR30 (G-coupled ER1), HAP1, HER3, HSP-27, HSP70, IC3b, IL8, insig, junction plakoglobin, Keratin 15, KRAS, Mammaglobin, MART1, MCT2, MFGE8, MMP9, MRP8, Muc1, MUC17, MUC2, NCAM, NG2 (CSPG4), Nga1, NHE-3, NT5E (CD73), ODC1, OPG, OPN, p53, PARK7, PCSA, PGP9.5 (PARKS), PR(B), PSA, PSMA, RAGE, STXBP4, Survivin, TFF3 (secreted), TIMP1, TIMP2, TMEM211, TRAF4 (scaffolding), TRAIL-R2 (death Receptor 5), TrkB, Tsg 101, UNC93a, VEGF A, VEGFR2, YB-1, VEGFR1, GCDPF-15 (PIP), BigH3 (TGFb1-induced protein), 5HT2B (serotonin receptor 2B), BRCA2, BACE 1, CDH1-cadherin, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer.

In yet another aspect, the invention provides a biosignature comprising determining a level of one or more biomarker in a biological sample. The one or more biomarker can be selected from the group consisting of AK5.2, ATP6V1B1, CRABP1, and a combination thereof. The one or more biomarker can be selected from the group consisting of DST.3, GATA3, KRT81, and a combination thereof. The one or more biomarker can be selected from the group consisting of AK5.2, ATP6V1B1, CRABP1, DST.3, ELF5, GATA3, KRT81, LALBA, OXTR, RASL10A, SERHL, TFAP2A.1, TFAP2A.3, TFAP2C, VTCN1, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer.

In an aspect, the invention provides a method of detecting a biosignature comprising determining a level of one or more biomarker in a biological sample, wherein the one or more biomarker is selected from the group consisting of a biomarker in Table 89, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer, such as a non-DCIS breast cancer. In a related aspect, the invention provides a method of detecting a biosignature comprising determining a level of one or more biomarker in a biological sample, wherein the one or more biomarker is selected from the group consisting of a biomarker in Table 90, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer, such as a DCIS breast cancer. In another related aspect, the invention provides a method of detecting a biosignature comprising determining a level of one or more biomarker in a biological sample, wherein the one or more biomarker is selected from the group consisting of a biomarker in Table 91, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a breast cancer, such as a non-DCIS or a DCIS breast cancer.

In an aspect, the invention provides a method of detecting a biosignature comprising determining a level of one or more biomarker in a biological sample, wherein the one or more biomarker is selected from the group consisting of a biomarker in Table 92, and a combination thereof. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a lung cancer, such as a non small cell lung cancer.

In another aspect, the invention provides a method of detecting a microvesicle population comprising: a) providing a biological sample suspected of comprising the microvesicle population; b) contacting the sample with one or more binding agent to one or more biomarker in the methods above; and c) detecting microvesicles associated with the one or more biomarker that have been contacted with the one or more binding agent, thereby detecting the microvesicle population.

In an aspect, the invention provides a method of detecting a biosignature comprising determining a level of one or more biomarker in a biological sample. The one or more biomarker can be selected from the group consisting of hsa-miR-125a-5p, hsa-miR-650, hsa-miR-194, hsa-miR-1200, hsa-miR-326, hsa-miR-30b*, hsa-miR-19a, hsa-miR-7a*, hsa-miR-708*, hsa-miR-99a, hsa-miR-199b-5p, hsa-miR-543, hsa-miR-7i*, hsa-miR-518c*, hsa-miR-642, hsa-miR-654-3p, hsa-miR-518d-5p, hsa-miR-1266, hsa-miR-154, hsa-miR-662, hsa-miR-523, hsa-miR-198, hsa-miR-920, hsa-miR-885-3p, hsa-miR-99a*, hsa-miR-337-3p, hsa-miR-363, and a combination thereof. The one or more biomarker can comprise miR-497. The biological sample can be a known or suspected cancer sample. The cancer can be a cancer as disclosed herein, including without limitation a lung cancer.

In the methods above, the one or more biomarker can include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, or more of the listed biomarkers. The one or more biomarker may comprise any measurable biological entity, including without limitation a protein, a nucleic acid, or a combination thereof. For example, the one or more biomarker can be a peptide, polypeptide, protein, or fragment thereof. Alternately the one or more biomarker can be a nucleic acid such as DNA or RNA, including without limitation mRNA, or fragments thereof. The one or more biomarker can also comprise a combination of biological entities, e.g., at least one protein and at least one nucleic acid.

As noted above, the biological sample may comprise a known or suspected cancer sample. In some embodiments, the biological sample comprises a cancer cell culture or a sample from a subject having or suspected of having the cancer. The cancer can be a cancer disclosed herein, including without limitation an acute lymphoblastic leukemia; acute myeloid leukemia; adrenocortical carcinoma; AIDS-related cancers; AIDS-related lymphoma; anal cancer; appendix cancer; astrocytomas; atypical teratoid/rhabdoid tumor; basal cell carcinoma; bladder cancer; brain stem glioma; brain tumor (including brain stem glioma, central nervous system atypical teratoid/rhabdoid tumor, central nervous system embryonal tumors, astrocytomas, craniopharyngioma, ependymoblastoma, ependymoma, medulloblastoma, medulloepithelioma, pineal parenchymal tumors of intermediate differentiation, supratentorial primitive neuroectodermal tumors and pineoblastoma); breast cancer; bronchial tumors; Burkitt lymphoma; cancer of unknown primary site; carcinoid tumor; carcinoma of unknown primary site; central nervous system atypical teratoid/rhabdoid tumor; central nervous system embryonal tumors; cervical cancer; childhood cancers; chordoma; chronic lymphocytic leukemia; chronic myelogenous leukemia; chronic myeloproliferative disorders; colon cancer; colorectal cancer; craniopharyngioma; cutaneous T-cell lymphoma; endocrine pancreas islet cell tumors; endometrial cancer; ependymoblastoma; ependymoma; esophageal cancer; esthesioneuroblastoma; Ewing sarcoma; extracranial germ cell tumor; extragonadal germ cell tumor; extrahepatic bile duct cancer; gallbladder cancer; gastric (stomach) cancer; gastrointestinal carcinoid tumor; gastrointestinal stromal cell tumor; gastrointestinal stromal tumor (GIST); gestational trophoblastic tumor; glioma; hairy cell leukemia; head and neck cancer; heart cancer; Hodgkin lymphoma; hypopharyngeal cancer; intraocular melanoma; islet cell tumors; Kaposi sarcoma; kidney cancer; Langerhans cell histiocytosis; laryngeal cancer; lip cancer; liver cancer; lung cancer; malignant fibrous histiocytoma bone cancer; medulloblastoma; medulloepithelioma; melanoma; Merkel cell carcinoma; Merkel cell skin carcinoma; mesothelioma; metastatic squamous neck cancer with occult primary; mouth cancer; multiple endocrine neoplasia syndromes; multiple myeloma; multiple myeloma/plasma cell neoplasm; mycosis fungoides; myelodysplastic syndromes; myeloproliferative neoplasms; nasal cavity cancer; nasopharyngeal cancer; neuroblastoma; Non-Hodgkin lymphoma; nonmelanoma skin cancer; non-small cell lung cancer; oral cancer; oral cavity cancer; oropharyngeal cancer; osteosarcoma; other brain and spinal cord tumors; ovarian cancer; ovarian epithelial cancer; ovarian germ cell tumor; ovarian low malignant potential tumor; pancreatic cancer; papillomatosis; paranasal sinus cancer; parathyroid cancer; pelvic cancer; penile cancer; pharyngeal cancer; pineal parenchymal tumors of intermediate differentiation; pineoblastoma; pituitary tumor; plasma cell neoplasm/multiple myeloma; pleuropulmonary blastoma; primary central nervous system (CNS) lymphoma; primary hepatocellular liver cancer; prostate cancer; rectal cancer; renal cancer; renal cell (kidney) cancer; renal cell cancer; respiratory tract cancer; retinoblastoma; rhabdomyosarcoma; salivary gland cancer; Sézary syndrome; small cell lung cancer; small intestine cancer; soft tissue sarcoma; squamous cell carcinoma; squamous neck cancer; stomach (gastric) cancer; supratentorial primitive neuroectodermal tumors; T-cell lymphoma; testicular cancer; throat cancer; thymic carcinoma; thymoma; thyroid cancer; transitional cell cancer; transitional cell cancer of the renal pelvis and ureter; trophoblastic tumor; ureter cancer; urethral cancer; uterine cancer; uterine sarcoma; vaginal cancer; vulvar cancer; Waldenström macroglobulinemia; or Wilm's tumor.

The methods above may further comprise comparing the presence or level of the one or more biomarker to a reference, wherein an altered presence or level relative to the reference provides a diagnostic, prognostic, or theranostic determination for the cancer. The diagnostic, prognostic, or theranostic determination for the cancer may comprise a diagnosis of the cancer or a likelihood of cancer, a prognosis of the cancer, a theranosis of the cancer, determining whether the cancer is responding to a therapeutic treatment, or determining whether the cancer is likely to respond to a therapeutic treatment. In embodiment, the therapeutic treatment is selected from Tables 10-13 or 69. The reference can be from a biological sample without the cancer. The reference can be from a series of biological samples measured at one or more different time point. In embodiments, elevated levels of the one or more biomarker in the sample as compared to the reference indicate the presence of or the likelihood of a cancer in the sample, or the presence of or the likelihood of a more advanced cancer in the sample.

In the methods above, the biological sample can be a bodily fluid or a derivative or fraction of a bodily fluid. In various embodiments, the bodily fluid comprises peripheral blood, sera, plasma, ascites, urine, cerebrospinal fluid (CSF), sputum, saliva, bone marrow, synovial fluid, aqueous humor, amniotic fluid, cerumen, breast milk, broncheoalveolar lavage fluid, semen, prostatic fluid, cowper's fluid or pre-ejaculatory fluid, female ejaculate, sweat, fecal matter, hair, tears, cyst fluid, pleural and peritoneal fluid, pericardial fluid, lymph, chyme, chyle, bile, interstitial fluid, menses, pus, sebum, vomit, vaginal secretions, mucosal secretion, stool water, pancreatic juice, lavage fluids from sinus cavities, bronchopulmonary aspirates, blastocyl cavity fluid, or umbilical cord blood. The bodily fluid can comprise blood or a blood derivative. In other embodiments, the biological sample is a tissue sample, e.g., a biopsy or surgical sample.

In embodiments of the methods above, the one or more biomarker are from a microvesicle population. The microvesicle population can comprise any vesicles described herein or known in the art, e.g., exosomes, microvesicles, ectosomes, membrane particles, exosome-like vesicles, or apoptotic vesicles as in Table 2. In an embodiment, the microvesicle population comprises vesicles having a diameter between 10 nm and 1000 nm. For example, the microvesicle population can comprise microvesicles having a diameter between 20 nm and 200 nm, between 50-100 nm, between 100-1,000 nm, between 50-200 nm, between 50-80 nm, between 20-50 nm, or between 50-500 nm.

The microvesicle population can be isolated in whole or in part from the biological sample by size exclusion chromatography, density gradient centrifugation, differential centrifugation, nanomembrane ultrafiltration, immunoabsorbent capture, affinity purification, affinity capture, affinity selection, immunoassay, immunoprecipitation, microfluidic separation, flow cytometry or combinations thereof.

In embodiment, the microvesicle population is contacted with one or more binding agent. The one or more binding agent comprises a nucleic acid, DNA molecule, RNA molecule, antibody, antibody fragment, aptamer, peptoid, zDNA, peptide nucleic acid (PNA), locked nucleic acid (LNA), lectin, peptide, dendrimer, membrane protein labeling agent, chemical compound, or a combination thereof. The one or more binding agent can be used to capture and/or detect the microvesicle population. In an embodiment, the one or more binding agent is bound to a substrate, including without limitation a well, a microbead and/or an array. The one or more binding agent can also carry a label such as described herein or known in the art, including without limitation a magnetic label, a fluorescent label, an enzymatic label, a radioisotope, a quantum dot, or a combination thereof.

In embodiments of the methods above, the one or more biomarker comprises a microvesicle surface antigen or functional fragment thereof. The microvesicle population can be captured with the one or more binding agent to the one or more biomarker and detected with a labeled binding agent to a biomarker that is selected from the group consisting of a tetraspanin, CD9, CD31, CD63, CD81, CD82, CD37, CD53, Rab-5b, Annexin V, MFG-E8, a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-14, 22, 26, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof.

Embodiments of the methods above further comprise detecting the level of a payload within the microvesicle population. The detected payload can be any measureable biological entity within a vesicle, including without limitation one or more nucleic acid, peptide, protein, lipid, antigen, carbohydrate, and/or proteoglycan. The detected payload may comprise one or more biomarker selected from the group consisting of a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-14, 22, 26, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof. Nucleic acid biomarkers may comprise one or more DNA, mRNA, microRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, or shRNA. For example, the nucleic acid can include one or more microRNA selected from the group consisting of microRNAs in any of Tables 5-9, 30-44, 58-59, 71 and 73. Nucleic acid biomarkers may also comprise one or more mRNA selected from the group consisting of a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-17, 19-22, 22, 26, 28-29, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof. Protein biomarkers can comprise one or more peptide, polypeptide, protein or fragment thereof selected from the group consisting of a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-17, 19-22, 22, 26, 28-29, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof.

The methods above may further comprise assaying the biological sample for at least one additional biomarker that is selected from the group consisting of a tetraspanin, CD9, CD31, CD63, CD81, CD82, CD37, CD53, Rab-5b, Annexin V, MFG-E8, a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-14, 22, 26, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof. The one or more additional biomarker can be detected using any useful method comprised herein or known in the art.

The methods above can be performed in vitro. In a related aspect, the invention provides use of one or more reagent to carry out the methods. Similarly, the invention provides a kit comprising one or more reagent to carry out the methods. The one or more reagent can comprise one or more binding agent to the one or more biomarker in the methods. The one or more reagent can also be one or more binding agent to one or more biomarker selected from the group consisting of a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-14, 22, 26, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof. In an embodiment, the one or more binding agent comprises an antibody or aptamer. The one or more binding agent can be tethered to a substrate. The one or more binding agent can be labeled. The one or more binding agent can comprise multiple binding agents in various forms, e.g., one or more binding agent can be tethered to a substrate and separately one or more labeled binding agent. The label can be any useful label described herein or known in the art, e.g., a magnetic label, a fluorescent label, an enzymatic label, a radioisotope, or a quantum dot.

In an aspect, the invention provides an isolated vesicle comprising one or more biomarker selected from the group consisting of the biomarkers listed in the methods above, and a combination thereof. In an embodiment, the vesicle comprises one or more biomarker selected from the group consisting of a biomarker in any of FIGS. 1-60, or Tables 3-10, 12-14, 22, 26, 45-50, 52, 54-57, 60-64, 66, 67, 69-70, 74-85, 89-92, and a combination thereof.

EXAMPLES Example 1 Purification of Vesicles from Prostate Cancer Cell Lines

Prostate cancer cell lines are cultured for 3-4 days in culture media containing 20% FBS (fetal bovine serum) and 1% P/S/G. The cells are then pre-spun for 10 minutes at 400×g at 4° C. The supernatant is kept and centrifuged for 20 minutes at 2000×g at 4. The supernatant containing vesicles can be concentrated using a Millipore Centricon Plus-70 (Cat # UFC710008 Fisher).

The Centricon is pre washed with 30 mls of PBS at 1000×g for 3 minutes at room temperature. Next, 15-70 mls of the pre-spun cell culture supernatant is poured into the Concentrate Cup and is centrifuged in a Swing Bucket Adapter (Fisher Cat #75-008-144) for 30 minutes at 1000×g at room temperature.

The flow through in the Collection Cup is poured off. The volume in the Concentrate Cup is brought back up to 60 mls with any additional supernatant. The Concentrate Cup is centrifuged for 30 minutes at 1000×g at room temperature to concentrate the cell supernatant.

The Concentrate Cup is washed by adding 70 mls of PBS and centrifuged for 30-60 minutes at 1000×g until approximately 2 mls remains. The vesicles are removed from the filter by inverting the concentrate into the small sample cup and centrifuge for 1 minute at 4° C. The volume is brought up to 25 mls with PBS. The vesicles are now concentrated and are added to a 30% Sucrose Cushion.

To make a cushion, 4 mls of Tris/30% Sucrose/D2O solution (30 g protease-free sucrose, 2.4 g Tris base, 50 ml D2O, adjust pH to 7.4 with 10N NCL drops, adjust volume to 100 mls with D2O, sterilize by passing thru a 0.22-um filter) is loaded to the bottom of a 30 ml V bottom thin walled Ultracentrifuge tube. The diluted 25 mls of concentrated vesicles is gently added above the sucrose cushion without disturbing the interface and is centrifuged for 75 minutes at 100,000×g at 4° C. The ˜25 mls above the sucrose cushion is carefully removed with a 10 ml pipet and the ˜3.5 mls of vesicles is collected with a fine tip transfer pipet (SAMCO 233) and transferred to a fresh ultracentrifuge tube, where 30 mls PBS is added. The tube is centrifuged for 70 minutes at 100,000×g at 4° C. The supernatant is poured off carefully. The pellet is resuspended in 200 ul PBS and can be stored at 4° C. or used for assays. A BCA assay (1:2) can be used to determine protein content and Western blotting or electron micrography can be used to determine vesicle purification.

Example 2 Purification of Vesicles from VCaP and 22Rv1

Vesicles from Vertebral-Cancer of the Prostate (VCaP) and 22Rv1, a human prostate carcinoma cell line, derived from a human prostatic carcinoma xenograft (CWR22R) were collected by ultracentrifugation by first diluting plasma with an equal volume of PBS (1 ml). The diluted fluid was transferred to a 15 ml falcon tube and centrifuged 30 minutes at 2000×g 4° C. The supernatant (˜2 mls) was transferred to an ultracentrifuge tube 5.0 ml PA thinwall tube (Sorvall #03127) and centrifuged at 12,000×g, 4° C. for 45 minutes.

The supernatant (˜2 mls) was transferred to a new 5.0 ml ultracentrifuge tubes and filled to maximum volume with addition of 2.5 mls PBS and centrifuged for 90 minutes at 110,000×g, 4° C. The supernatant was poured off without disturbing the pellet and the pellet resuspended with 1 ml PBS. The tube was filled to maximum volume with addition of 4.5 ml of PBS and centrifuged at 110,000×g, 4° C. for 70 minutes.

The supernatant was poured off without disturbing the pellet and an additional 1 ml of PBS was added to wash the pellet. The volume was increased to maximum volume with the addition of 4.5 mls of PBS and centrifuged at 110,000×g for 70 minutes at 4° C. The supernatant was removed with P-1000 pipette until ˜100 μl of PBS was in the bottom of the tube. The ˜90 μl remaining was removed with P-200 pipette and the pellet collected with the ˜10 μl of PBS remaining by gently pipetting using a P-20 pipette into the microcentrifuge tube. The residual pellet was washed from the bottom of a dry tube with an additional 5 μl of fresh PBS and collected into microcentrifuge tube and suspended in phosphate buffered saline (PBS) to a concentration of 500 μg/ml.

Example 3 Plasma Collection and Vesicle Purification

Blood is collected via standard veinpuncture in a 7 ml K2-EDTA tube. The sample is spun at 400 g for 10 minutes in a 4° C. centrifuge to separate plasma from blood cells (SORVALL Legend RT+ centrifuge). The supernatant (plasma) is transferred by careful pipetting to 15 ml Falcon centrifuge tubes. The plasma is spun at 2,000 g for 20 minutes and the supernatant is collected.

For storage, approximately 1 ml of the plasma (supernatant) is aliquoted to a cryovials, placed in dry ice to freeze them and stored in −80° C. Before vesicle purification, if samples were stored at −80° C., samples are thawed in a cold water bath for 5 minutes. The samples are mixed end over end by hand to dissipate insoluble material.

In a first prespin, the plasma is diluted with an equal volume of PBS (example, approximately 2 ml of plasma is diluted with 2 ml of PBS). The diluted fluid is transferred to a 15 ml Falcon tube and centrifuged for 30 minutes at 2000×g at 4° C.

For a second prespin, the supernatant (approximately 4 mls) is carefully transferred to a 50 ml Falcon tube and centrifuged at 12,000×g at 4° C. for 45 minutes in a Sorval.

In the isolation step, the supernatant (approximately 2 mls) is carefully transferred to a 5.0 ml ultracentrifuge PA thinwall tube (Sorvall #03127) using a P1000 pipette and filled to maximum volume with an additional 0.5 mls of PBS. The tube is centrifuged for 90 minutes at 110,000×g at 4° C.

In the first wash, the supernatant is poured off without disturbing the pellet. The pellet is resuspended or washed with 1 ml PBS and the tube is filled to maximum volume with an additional 4.5 ml of PBS. The tube is centrifuged at 110,000×g at 4° C. for 70 minutes. A second wash is performed by repeating the same steps.

The vesicles are collected by removing the supernatant with P-1000 pipette until approximately 100 μl of PBS is in the bottom of the tube. Approximately 90 μl 1 of the PBS is removed and discarded with P-200 pipette. The pellet and remaining PBS is collected by gentle pipetting using a P-20 pipette. The residual pellet is washed from the bottom of the dry tube with an additional 5 μl of fresh PBS and collected into a microcentrifuge tube.

Example 4 Analysis of Vesicles Using Antibody-Coupled Microspheres and Directly Conjugated Antibodies

This example demonstrates the use of particles coupled to an antibody, where the antibody captures the vesicles. See, e.g., FIG. 63A. An antibody, the detector antibody, is directly coupled to a label, and is used to detect a biomarker on the captured vesicle.

First, an antibody-coupled microsphere set is selected (Luminex, Austin, Tex.). The microsphere set can comprise various antibodies, and thus allows multiplexing. The microspheres are resuspended by vortex and sonication for approximately 20 seconds. A Working Microsphere Mixture is prepared by diluting the coupled microsphere stocks to a final concentration of 100 microspheres of each set/4 in Startblock (Pierce (37538)). 50 μL of Working Microsphere Mixture is used for each well. Either PBS-1% BSA or PBS-BN (PBS, 1% BSA, 0.05% Azide, pH 7.4) may be used as Assay Buffer.

A 1.2 μm Millipore filter plate is pre-wet with 100 μl/well of PBS-1% BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and aspirated by vacuum manifold. An aliquot of 50 μl of the Working Microsphere Mixture is dispensed into the appropriate wells of the filter plate (Millipore Multiscreen HTS (MSBVN1250)). A 50 μl aliquot of standard or sample is dispensed into to the appropriate wells. The filter plate is covered and incubated for 60 minutes at room temperature on a plate shaker. The plate is covered with a sealer, placed on the orbital shaker and set to 900 for 15-30 seconds to re-suspend the beads. Following that the speed is set to 550 for the duration of the incubation.

The supernatant is aspirated by vacuum manifold (less than 5 inches Hg in all aspiration steps). Each well is washed twice with 100 μl of PBS-1% BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and is aspirated by vacuum manifold. The microspheres are resuspended in 50 μL of PBS-1% BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))). The PE conjugated detection antibody is diluted to 4 μg/mL (or appropriate concentration) in PBS-1% BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))). (Note: 50 μL of diluted detection antibody is required for each reaction.) A 50 μl aliquot of the diluted detection antibody is added to each well. The filter plate is covered and incubated for 60 minutes at room temperature on a plate shaker. The filter plate is covered with a sealer, placed on the orbital shaker and set to 900 for 15-30 seconds to re-suspend the beads. Following that the speed is set to 550 for the duration of the incubation. The supernatant is aspirated by vacuum manifold. The wells are washed twice with 100 μl of PBS-1% BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and aspirated by vacuum manifold. The microspheres are resuspended in 100 μl of PBS-1% BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))). The microspheres are analyzed on a Luminex analyzer according to the system manual.

FIG. 66 depicts antibody-conjugated microspheres with vesicles bound thereto. Specifically, the figure shows scanning electron micrographs (SEMs) of EpCam conjugated beads that have been incubated with VCaP vesicles. For the graphic shown in FIG. 66A, a glass slide was coated with poly-L-lysine and incubated with the bead solution. After attachment, the beads were (i) fixed sequentially with glutaraldehyde and osmium tetroxide, 30 min per fix step with a few washes in between; (ii) gradually dehydrated in acetone, 20% increments, about 5-7 min per step; (iii) critical-point dried; and (iv) sputter-coated with gold. FIG. 66B, left depicts a higher magnification of vesicles on an EpCam coated bead as in FIG. 66A. FIG. 66B, right depicts vesicles isolated by ultracentrifugation and adhered to a poly-L-lysine coated glass slide and fixed and stained as in FIG. 66A.

Example 5 Analysis of Vesicles Using Antibody-Coupled Microspheres and Biotinylated Antibody

This example demonstrates the use of particles coupled to an antibody, where the antibody captures the vesicles. An antibody, the detector antibody, is biotinylated. A label coupled to streptavidin is used to detect the biomarker.

First, the appropriate antibody-coupled microsphere set is selected (Luminex, Austin, Tex.). The microspheres are resuspended by vortex and sonication for approximately 20 seconds. A Working Microsphere Mixture is prepared by diluting the coupled microsphere stocks to a final concentration of 50 microspheres of each set/4 in Startblock (Pierce (37538)). (Note: 50 μl of Working Microsphere Mixture is required for each well.) Beads in Start Block should be blocked for 30 minutes and no more than 1 hour.

A 1.2 μm Millipore filter plate is pre-wet with 100 μl/well of PBS-1% BSA+Azide (PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and is aspirated by vacuum manifold. A 50 μl aliquot of the Working Microsphere Mixture is dispensed into the appropriate wells of the filter plate (Millipore Multiscreen HTS (MSBVN1250)). A 50 μl aliquot of standard or sample is dispensed to the appropriate wells. The filter plate is covered with a seal and is incubated for 60 minutes at room temperature on a plate shaker. The covered filter plate is placed on the orbital shaker and set to 900 for 15-30 seconds to re-suspend the beads. Following that, the speed is set to 550 for the duration of the incubation.

The supernatant is aspirated by a vacuum manifold (less than 5 inches Hg in all aspiration steps). Aspiration can be done with the Pall vacuum manifold. The valve is place in the full off position when the plate is placed on the manifold. To aspirate slowly, the valve is opened to draw the fluid from the wells, which takes approximately 3 seconds for the 100 μl of sample and beads to be fully aspirated from the well. Once the sample drains, the purge button on the manifold is pressed to release residual vacuum pressure from the plate.

Each well is washed twice with 100 μl of PBS-1% BSA+Azide (PBS-BN)(Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and is aspirates by vacuum manifold. The microspheres are resuspended in 50 μl of PBS-1% BSA+Azide (PBS-BN)((Sigma (P3688-10PAK+0.05% NaAzide (S8032)))

The biotinylated detection antibody is diluted to 4 μg/mL in PBS-1% BSA+Azide (PBS-BN)((Sigma (P3688-10PAK+0.05% NaAzide (S8032))). (Note: 50 μl of diluted detection antibody is required for each reaction.) A 50 μl aliquot of the diluted detection antibody is added to each well.

The filter plate is covered with a sealer and is incubated for 60 minutes at room temperature on a plate shaker. The plate is placed on the orbital shaker and set to 900 for 15-30 seconds to re-suspend the beads. Following that, the speed is set to 550 for the duration of the incubation.

The supernatant is aspirated by vacuum manifold. Aspiration can be done with the Pall vacuum manifold. The valve is place in the full off position when the plate is placed on the manifold. To aspirate slowly, the valve is opened to draw the fluid from the wells, which takes approximately 3 seconds for the 100 ul of sample and beads to be fully aspirated from the well. Once all of the sample is drained, the purge button on the manifold is pressed to release residual vacuum pressure from the plate.

Each well is washed twice with 100 μl of PBS-1% BSA+Azide (PBS-BN)((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and is aspirated by vacuum manifold. The microspheres are resuspended in 50 μl of PBS-1% BSA (Sigma (P3688-10PAK+0.05% NaAzide (S8032))).

The streptavidin-R-phycoerythrin reporter (Molecular Probes 1 mg/ml) is diluted to 4 μg/mL in PBS-1% BSA+Azide (PBS-BN). 50 μl of diluted streptavidin-R-phycoerythrin was used for each reaction. A 50 μl aliquot of the diluted streptavidin-R-phycoerythrin is added to each well.

The filter plate is covered with a sealer and is incubated for 60 minutes at room temperature on a plate shaker. The plate is placed on the orbital shaker and set to 900 for 15-30 seconds to re-suspend the beads. Following that, the speed is set to 550 for the duration of the incubation.

The supernatant is aspirated by vacuum manifold. Aspiration can be done with the Pall vacuum manifold. The valve is place in the full off position when the plate is placed on the manifold. To aspirate slowly, the valve is opened to draw the fluid from the wells, which takes approximately 3 seconds for the 100 ul of sample and beads to be fully aspirated from the well. Once all of the sample is drained, the purge button on the manifold is pressed to release residual vacuum pressure from the plate.

Each well is washed twice with 100 μl of PBS-1% BSA+Azide (PBS-BN)((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and is aspirated by vacuum manifold. The microspheres are resuspended in 100 μl of PBS-1% BSA+Azide (PBS-BN)((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) and analyzed on the Luminex analyzer according to the system manual.

Example 6 Antibody Purification and Carbodiimide Coupling to Carboxylated Microspheres

Antibody Purification Protocol: Antibodies were purified using Protein G resin from Pierce (Protein G spin kit, Product #89979, Pierce, a part of Thermo Scientific, Rockford, Ill.). Micro-chromatography columns made from filtered P-200 tips were used for purification.

One hundred μl of Protein G resin is loaded with 100 μl buffer from the Pierce kit to each micro column. After waiting a few minutes to allow the resin to settle down, air pressure is applied with a P-200 Pipettman to drain buffer when needed, ensuring the column is not let to dry. The column is equilibrated with 0.6 ml of Binding Buffer (pH 7.4, 100 mM Phosphate Buffer, 150 mM NaCl; (Pierce, Prod #89979). An antibody is applied to the column (<1 mg of antibody is loaded on the column). The column is washed with 1.5 ml of Binding Buffer. Five tubes (1.5 ml micro centrifuge tubes) are prepared and 10 μl of neutralization solution (Pierce, Prod #89979) is applied to each tube. The antibody is eluted with the elution buffer from the kit to each of the five tubes, 100 ul for each tube (for a total of 500 μl). The relative absorbance of each fraction is measured at 280 nm using Nanodrop (Nanodrop 1000 spectrophotometer, Nanodrop, a part of Thermo Scientific, Wilmington, Del.). The fractions with highest OD reading are selected for downstream usage. The samples are dialyzed against 0.25 liters PBS buffer using Pierce Slide-A-Lyzer Dialysis Cassette (Pierce, Product No. 66333, 3 KDa cut off). The buffer is exchanged every 2 hours for minimum three exchanges at 4° C. with continuous stifling. The dialyzed samples are then transferred to 1.5 ml microcentifuge tubes, and can be labeled and stored at 4° C. (short term) or −20° C. (long term).

Coupling:

Microspheres are coated with their respective antibodies as listed above according to the following protocol.

The microspheres are protected from prolonged exposure to light throughout this procedure. The stock uncoupled microspheres are resuspended according to the instructions described in the Product Information Sheet provided with the microspheres (xMAP technologies, MicroPlex™ Microspheres, Luminex Corporation, Austin, Tex.). Five ×106 of the stock microspheres are transferred to a USA Scientific 1.5 ml microcentrifuge tube (USA Scientific, Inc., Orlando, Fla.). The stock microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature. The supernatant is removed and the pelleted microspheres are resuspended in 100 μl of dH2O by vortex and sonication for approximately 20 seconds. The microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature. The supernatant is removed and the washed microspheres are resuspended in 80 μl of 100 mM Monobasic Sodium Phosphate, pH 6.2 by vortex and sonication (Branson 1510, Branson Ultrasonics Corp., Danbury, Conn.) for approximately 20 seconds. Ten μl of 50 mg/ml Sulfo-NHS (Thermo Scientific, Cat#24500) (diluted in dH20) is added to the microspheres and is mixed gently by vortex. Ten μl of 50 mg/ml EDC (Thermo Scientific, Cat#25952-53-8) (diluted in dH20) is added to the microspheres and gently mixed by vortexing. The microspheres are incubated for 20 minutes at room temperature with gentle mixing by vortex at 10 minute intervals. The activated microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature. The supernatant is removed and the microspheres are resuspended in 250 μl of 50 mM MES, pH 5.0 (MES, Sigma, Cat# M2933, Sigma-Aldrich, St. Louis, Mo.) by vortex and sonication for approximately 20 seconds. Only PBS-1% BSA+Azide (PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) should be used as assay buffer as well as wash buffer. The microspheres are then pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature.

The supernatant is removed and the microspheres are resuspended in 250 μl of 50 mM MES, pH 5.0 (MES, Sigma, Cat# M2933) by vortex and sonication for approximately 20 seconds. Only PBS-1% BSA+Azide (PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) should be used as assay buffer as well as wash buffer. The microspheres are then pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature, thus completing two washes with 50 mM MES, pH 5.0.

The supernatant is removed and the activated and washed microspheres are resuspended in 100 μl of 50 mM MES, pH 5.0 by vortex and sonication for approximately 20 seconds. Protien in the amount of 125, 25, 5 or 1 μg is added to the resuspended microspheres. (Note: Titration in the 1 to 125 μg range can be performed to determine the optimal amount of protein per specific coupling reaction.). The total volume is brought up to 500 μl with 50 mM MES, pH 5.0. The coupling reaction is mixed by vortex and is incubated for 2 hours with mixing (by rotating on Labquake rotator, Barnstead, Thermo Scientific) at room temperature. The coupled microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature. The supernatant is removed and the pelleted microspheres are resuspended in 5004 of PBS-TBN by vortex and sonication for approximately 20 seconds. (Concentrations can be optimized for specific reagents, assay conditions, level of multiplexing, etc. in use.).

The microspheres are incubated for 30 minutes with mixing (by rotating on Labquake rotator, Barnstead) at room temperature. The coupled microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature. The supernatant is removed and the microspheres are resuspended in 1 ml of PBS-TBN by vortex and sonication for approximately 20 seconds. Each time there is the addition of samples, detector antibody or SA-PE the plate is covered with a sealer and light blocker (such as aluminum foil), placed on the orbital shaker and set to 900 for 15-30 seconds to re-suspend the beads. Following that the speed should be set to 550 for the duration of the incubation.

The microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes. The supernatant is removed and the microspheres are resuspended in 1 ml of PBS-TBN by vortex and sonication for approximately 20 seconds. The microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes (resulting in a total of two washes with 1 ml PBS-TBN).

Example 7 Vesicle Concentration from Plasma

Supplies and Equipment:

Pall life sciences Acrodisc, 25 mm syringe filter w/1.2 um, Versapor membrane (sterile) Part number: 4190; Pierce concentrators 7 ml/150 K MWCO (molecular weight cut off), Part number: 89922; BD syringe filter, 10 ml, Part number: 305482; Sorvall Legend RT Plus Series Benchtop Centrifuge w 15 ml swinging bucket rotor; PBS, pH 7.4, Sigma cat#P3813-10PAK prepared in Sterile Molecular grade water; Co-polymer 1.7 ml microfuge tubes, USA Scientific, cat#1415-2500. Water used for reagents is Sterile Filtered Molecular grade water (Sigma, cat#W4502). Handling of patient plasma is done in a biosafety hood.

Procedure:

1. Filter procedure for plasma samples

-   -   1.1 Remove plasma samples from −80° C. (−65° C. to −85° C.)         freezer     -   1.2. Thaw samples in room temperature water (10-15 minutes).     -   1.3. Prepare syringe and filter by removing the number necessary         from their casing.     -   1.4. Pull plunger to draw 4 mL of sterile molecular grade water         into the syringe. Attach a 1.2 μm filter to the syringe tip and         pass contents through the filter onto the 7 ml/150 K MWCO Pierce         column.     -   1.5. Cap the columns and place in the swing bucket centrifuge at         spin at 1000×g in Sorvall Legend RT plus centrifuge for 4         minutes at 20° C. (16° C.-24° C.).     -   1.6. While spinning, disassemble the filter from syringe. Then         remove plunger from syringe.     -   1.7. Discard flow through from the tube and gently tap column on         paper towels to remove any residual water.     -   1.8. Measure and record starting volumes for all plasma samples.         Samples with a volume less than 900 μl may not be processed.     -   1.9. Place open syringe and filter on open Pierce column. Fill         open end of syringe with 5.2 mL of 1×PBS and pipette mix plasma         into PBS three to four times.     -   1.10. Replace the plunger of the syringe and slowly depress the         plunger until the contents of the syringe have passed through         the filter onto the Pierce column. Contents should pass through         the filter drop wise.

2. Microvesicle concentration centrifugation protocol

-   -   2.1. Spin 7 ml/150 K MWCO Pierce columns at 2000×g at 20° C.         (16° C.-24° C.) for 60 minutes or until volume is reduced to         250-300 μL. If needed, spin for additional 15 minutes increments         to reach required volume.     -   2.2. At the conclusion of the spin, pipette mix on the column         15× (avoid creating bubbles) and withdraw volume (300 μL or         less) and transfer to a new 1.7 mL co-polymer tube.     -   2.3. The final volume of the plasma concentrate is dependent on         the initial volume of plasma. Plasma is concentrated to 300 ul         if the original plasma volume is 1 ml. If the original volume of         plasma is less than 1 ml, then the volume of concentrate should         be consistent with that ratio. For example, if the original         volume is 900 ul, then the volume of concentrate is 270 ul. The         equation to follow is: x=(y/1000)*300, where x is the final         volume of concentrate and y is the initial volume of plasma.     -   2.4. Record the sample volume and add 1×PBS to the sample to         make the final sample volume.     -   2.5. Store concentrated microvesicle sample at 4° C. (2° C. to         8° C.).

Calculations:

1. Final volume of concentrated plasma sample

-   -   x=(y/1000)*300, where x is the final volume of concentrate and y         is the initial volume of plasma.

Example 8 Determining Biosignatures for Prostate Cancer Using Multiplexing

The samples obtained using methods as described in Example 3 are used in multiplexing assays as described in Examples 4 and 5. The detection antibodies used are CD63, CD9, CD81, B7H3 and EpCam. The capture antibodies used are CD9, PSCA, TNFR, CD63 2X, B7H3, MFG-E8, EpCam 2X, CD63, Rab, CD81, SETAP, PCSA, PSMA, 5T4, Rab IgG (control) and IgG (control), resulting in 100 combinations to be screened (FIG. 63C).

Ten prostate cancer patients and 12 normal control patients were screened. Results for the indicated capture and/or detection antibodies are depicted in FIG. 68 and FIG. 70A. FIG. 70B depicts the results of using PCSA capture antibodies (FIG. 70B, left graph) or EpCam capture antibodies (FIG. 70B, right graph), and detection using one or more detector antibodies. The sensitivity and specificity of the different combinations is depicted in FIG. 73.

Example 9 Determining Biosignatures for Colon Cancer Using Multiplexing

Vesicle samples obtained using methods as described in Example 3 are used in multiplexing assays as described in Examples 4 and 5. The detection antibodies used are CD63, CD9, CD81, B7H3 and EpCam. The capture antibodies used are CD9, PSCA, TNFR, CD63 2X, B7H3, MFG-E8, EpCam 2X, CD63, Rab, CD81, STEAP, PCSA, PSMA, 5T4, Rab IgG (control) and IgG (control), resulting in 100 combinations to be screened.

The results are depicted in FIGS. 69, 71, and 72. The sensitivity of the different combinations is depicted in FIG. 74.

Example 10 Capture of Vesicles Using Magnetic Beads

Vesicles isolated as described in Example 2 are used. Approximately 40 ul of the vesicles are incubated with approximately 5 ug (˜50 μl) of EpCam antibody coated Dynal beads (Invitrogen, Carlsbad, Calif.) and 50 μl of Starting Block. The vesicles and beads are incubated with shaking for 2 hours at 45° C. in a shaking incubator. The tube containing the Dynal beads is placed on the magnetic separator for 1 minute and the supernatant removed. The beads are washed twice and the supernatant removed each time. Wash beads twice, discarding the supernatant each time.

Example 11 Detection of mRNA Transcripts in Vesicles

RNA from the bead-bound vesicles of Example 10 was isolated using the Qiagen miRneasy™ kit, (Cat. No. 217061), according to the manufacturer's instructions.

The vesicles are homogenized in QIAzol™ Lysis Reagent (Qiagen Cat. No. 79306). After addition of chloroform, the homogenate is separated into aqueous and organic phases by centrifugation. RNA partitions to the upper, aqueous phase, while DNA partitions to the interphase and proteins to the lower, organic phase or the interphase. The upper, aqueous phase is extracted, and ethanol is added to provide appropriate binding conditions for all RNA molecules from 18 nucleotides (nt) upwards. The sample is then applied to the RNeasy™ Mini spin column, where the total RNA binds to the membrane and phenol and other contaminants are efficiently washed away. High quality RNA is then eluted in RNase-free water.

RNA from the VCAP bead captured vesicles was measured with the Taqman TMPRSS:ERG fusion transcript assay (Kirsten D. Mertz et al. Neoplasia. 2007 March; 9(3): 200-206.). RNA from the 22Rv1 bead captured vesicles was measured with the Taqman SPINK1 transcript assay (Scott A. Tomlins et al. Cancer Cell 2008 June 13(6):519-528). The GAPDH transcript (control transcript) was also measured for both sets of vesicle RNA.

Higher CT values indicate lower transcript expression. One change in cycle threshold (CT) is equivalent to a 2 fold change, 3 CT difference to a 4 fold change, and so forth, which can be calculated with the following: 2̂^(CT1-CT2). This experiment shows a difference in CT of the expression of the fusion transcript TMPRSS:ERG and the equivalent captured with the IgG2 negative control bead (FIG. 75). The same comparison of the SPINK1 transcript in 22RV1 vesicles shows a CT difference of 6.14 for a fold change of 70.5 (FIG. 75C). Results with GAPDH were similar.

Example 12 Obtaining Serum Samples from Subjects

Blood is collected from subjects (both healthy subjects and subjects with cancer) in EDTA tubes, citrate tubes or in a 10 ml Vacutainer SST plus Blood Collection Tube (BD367985 or BD366643, BD Biosciences). Blood is processed for plasma isolation within 2 h of collection.

Samples are allowed to sit at room temperature for a minimum of 30 min and a max of 2 h. Separation of the clot is accomplished by centrifugation at 1,000-1,300×g at 4° C. for 15-20 min. The serum is removed and dispensed in aliquots of 500 μl into 500 to 750 μl cryotubes. Specimens are stored at −80° C.

At a given sitting, the amount of blood drawn can range from ˜20 to ˜90 ml. Blood from several EDTA tubes is pooled and transferred to RNase/DNase-free 50-ml conical tubes (Greiner), and centrifuged at 1,200×g at room temperature in a Hettich Rotanta 460R benchtop centrifuge for 10 min. Plasma is transferred to a fresh tube, leaving behind a fixed height of 0.5 cm plasma supernatant above the pellet to avoid disturbing the pellet. Plasma is aliquoted, with inversion to mix between each aliquot, and stored at −80° C.

Example 13 RNA Isolation from Human Plasma and Serum Samples

Four hundred μl of human plasma or serum is thawed on ice and lysed with an equal volume of 2× Denaturing Solution (Ambion). RNA is isolated using the mirVana PARIS kit following the manufacturer's protocol for liquid samples (Ambion), modified such that samples are extracted twice with an equal volume of acid-phenol chloroform (as supplied by the Ambion kit). RNA is eluted with 105 μl of Ambion elution solution according to the manufacturer's protocol. The average volume of eluate recovered from each column is about 80 μl.

A scaled-up version of the mirVana PARIS (Ambion) protocol is also used: 10 ml of plasma is thawed on ice, two 5-ml aliquots are transferred to 50-ml tubes, diluted with an equal volume of mirVana PARIS 2× Denaturing Solution, mixed thoroughly by vortexing for 30 s and incubated on ice for 5 min. An equal volume (10 ml) of acid/phenol/chloroform (Ambion) is then added to each aliquot. The resulting solutions are vortexed for 1 min and spun for 5 min at 8,000 rpm, 20° C. in a JA17 rotor. The acid/phenol/chloroform extraction is repeated three times. The resulting aqueous volume is mixed thoroughly with 1.25 volumes of 100% molecular-grade ethanol and passed through a mirVana PARIS column in sequential 700-μl aliquots. The column is washed following the manufacturer's protocol, and RNA is eluted in 105 μl of elution buffer (95° C.). A total of 1.5 μl of the eluate is quantified by Nanodrop.

Example 14 Measurement of miRNA Levels in RNA from Plasma and Serum using qRT-PCR

A fixed volume of 1.67 μl of RNA solution from about ˜80 μl-eluate from RNA isolation of a given sample is used as input into the reverse transcription (RT) reaction. For samples in which RNA is isolated from a 400-μl plasma or serum sample, for example, 1.67 μl of RNA solution represents the RNA corresponding to (1.67/80)×400=8.3 μl plasma or serum. For generation of standard curves of chemically synthesized RNA oligonucleotides corresponding to known miRNAs, varying dilutions of each oligonucleotide are made in water such that the final input into the RT reaction has a volume of 1.67 μl. Input RNA is reverse transcribed using the TaqMan miRNA Reverse Transcription Kit and miRNA-specific stem-loop primers (Applied BioSystems) in a small-scale RT reaction comprised of 1.387 μl of H2O, 0.5 μl of 10× Reverse-Transcription Buffer, 0.063 μl of RNase-Inhibitor (20 units/μl), 0.05 μl of 100 mM dNTPs with dTTP, 0.33 μl of Multiscribe Reverse-Transcriptase, and 1.67 μl of input RNA; components other than the input RNA can be prepared as a larger volume master mix, using a Tetrad2 Peltier Thermal Cycler (BioRad) at 16° C. for 30 min, 42° C. for 30 min and 85° C. for 5 min. Real-time PCR is carried out on an Applied BioSystems 7900HT thermocycler at 95° C. for 10 min, followed by 40 cycles of 95° C. for 15 s and 60° C. for 1 min. Data is analyzed with SDS Relative Quantification Software version 2.2.2 (Applied BioSystems.), with the automatic Ct setting for assigning baseline and threshold for Ct determination.

The protocol can also be modified to include a preamplification step, such as for detecting miRNA. A 1.25-μl aliquot of undiluted RT product is combined with 3.75 μl of Preamplification PCR reagents [comprised, per reaction, of 2.5 μl of TaqMan PreAmp Master Mix (2×) and 1.25 μl of 0.2× TaqMan miRNA Assay (diluted in TE)] to generate a 5.0-μl preamplification PCR, which is carried out on a Tetrad2 Peltier Thermal Cycler (BioRad) by heating to 95° C. for 10 min, followed by 14 cycles of 95° C. for 15 s and 60° C. for 4 min. The preamplification PCR product is diluted (by adding 20 μl of H2O to the 5-μl preamplification reaction product), following which 2.25 μl of the diluted material is introduced into the real-time PCR and carried forward as described.

Example 15 Generation of Standard Curves for Absolute Quantification of miRNAs

Synthetic single-stranded RNA oligonucleotides corresponding to the mature miRNA sequence (miRBase Release v.10.1) are purchased from Sigma. Synthetic miRNAs are input into the RT reaction over an empirically-derived range of copies to generate standard curves for each of the miRNA TaqMan assays listed above. In general, the lower limit of accurate quantification for each assay is designated based on the minimal number of copies input into an RT reaction that results in a Ct value within the linear range of the standard curve and that is also not equivalent to or higher than a Ct obtained from an RT input of lower copy number. A line is fit to data from each dilution series using Ct values within the linear range, from which y=m ln(x)+b equations are derived for quantification of absolute miRNA copies (x) from each sample Ct (y). Absolute copies of miRNA input into the RT reaction are converted to copies of miRNA per microliter plasma (or serum) based on the knowledge that the material input into the RT reaction corresponds to RNA from 2.1% of the total starting volume of plasma [i.e., 1.67 μl of the total RNA eluate volume (80 μl on average) is input into the RT reaction]. An example of a synthetic miRNA sequence is for miR-141 which can be obtained commercially such as from Sigma (St. Louis, Mo.).

Example 16 Extracting microRNA from Vesicles

MicroRNA is extracted from vesicles isolated from patient samples as described herein. See, e.g., Examples 7, 49. Methods for isolation and concentration of vesicles are presented herein. The methods in this Example can also be used to isolate microRNA from patient samples without first isolating vesicles.

Protocol Using Trizol

This protocol uses the QIAzol Lysis Reagent and RNeasy Midi Kit from Qiagen Inc., Valencia Calif. to extract microRNA from concentrated vesicles. The steps of the method comprise:

1. Add 2 μl of RNase A to 50 μl of vesicle concentrate, incubate at 37° C. for 20 min. 2. Add 700 μl of QIAzol Lysis Reagent, vortex 1 minute. Spike samples with 25 fmol/μL of C. elegans microRNA (1 μL) after the addition of QIAzol, making a 75 fmol/μL spike in for each total sample (3 aliquots combined).

3. Incubate at 55° C. for 5 min.

4. Add 140 μl chloroform and shake vigorously for 15 sec.

5. Cool on ice for 2-3 min. 6. Centrifuge @ 12,000×g at 4° C. for 15 min.

7. Transfer aqueous phase (300 μL) to a new tube and add 1.5 volumes of 100% EtOH (i.e., 450 μL). 8. Pipet up to 4 ml of sample into an RNeasy Midi spin column in a 15 ml collection tube (combining lysis from 3 50 μl of concentrate) 9. Spin at 2700×g for 5 min at room temperature. 10. Discard flowthrough from the spin. 11. Add 1 ml of Buffer RWT to column and centrifuge at 2700×g for 5 min at room temperature. Do not use Buffer RW1 supplied in the Midi kit. Buffer RW1 can wash away miRNA. Buffer RWT is supplied in the Mini kit from Qiagen Inc. 12. Discard flowthrough. 13. Add 1 ml of Buffer RPE onto the column and centrifuge at 2700×g for 2 min at room temperature. 14. Repeat steps 12 and 13. 16. Place column into a new 15 ml collection tube and add 150 ul Elution Buffer. Incubate at room temperature for 3 min. 17. Centrifuge at 2700×g for 3 min at room temperature. 18. Vortex the sample and transfer to 1.7 mL tube. Store the extracted sample at −80° C.

Protocol Using MagMax

This protocol uses the MagMAX™ RNA Isolation Kit from Applied Biosystems/Ambion, Austin, Tex. to extract microRNA from concentrated vesicles. The steps of the method comprise:

1. Add 700 ml of QIAzol Lysis Reagent and vortex 1 minute. 2. Incubate on benchtop at room temperature for 5 min. 3. Add 140 μl chloroform and shake vigorously for 15 sec. 4. Incubate on benchtop for 2-3 min.

5. Centrifuge at 12,000×g at 4° C. for 15 min

6. Transfer aqueous phase to a deep well plate and add 1.25 volumes of 100% Isopropanol. 7. Shake MagMAX™ binding beads well. Pipet 10 μl of RNA binding beads into each well. 8. Gather two elution plates and two additional deep well plates. 9. Label one elution plate “Elution” and the other “Tip Comb.” 10. Label one deep well as “1st Wash 2” and the other as “2nd Wash 2.” 11. Fill both Wash 2 deep well plates with 150 μl of Wash 2, being sure to add ethanol to wash beforehand. Fill in the same number of wells as there are samples. 12. Select the appropriate collection program on the MagMax Particle Processor. 13. Press start and load each appropriate plate. 14. Transfer samples to microcentrifuge tubes. 15. Vortex and store at −80° C. Residual beads will be seen in sample.

Example 17 MicroRNA Arrays

MicroRNA levels in a sample can be analyzed using an array format, including both high density and low density arrays. Array analysis can be used to discover differentially expressed in a desired setting, e.g., by analyzing the expression of a plurality of miRs in two samples and performing a statistical analysis to determine which ones are differentially expressed between the samples and can therefore be used in a biosignature. The arrays can also be used to identify a presence or level of one or more microRNAs in a single sample in order to characterize a phenotype by identifying a biosignature in the sample. This Example describes commercially available systems that are used to carry out the methods of the invention.

TaqMan Low Density Array

TaqMan Low Density Array (TLDA) miRNA cards are used to compare expression of miRNA in various sample groups as desired. The miRNA are collected and analyzed using the TaqMan® MicroRNA Assays and Arrays systems from Applied Biosystems, Foster City, Calif. Applied Biosystems TaqMan® Human MicroRNA Arrays are used according to the Megaplex™ Pools Quick Reference Card protocol supplied by the manufacturer.

Exiqon mIRCURY LNA microRNA

The Exiqon miRCURY LNA™ Universal RT microRNA PCR Human Panels I and II (Exiqon, Inc, Woburn, Mass.) are used to compare expression of miRNA in various sample groups as desired. The Exiqon 384 well panels include 750 miRs. Samples are normalized to control primers towards synthetic RNA spike-in from Universal cDNA synthesis kit (UniSp6 CP). Results were normalized to inter-plate calibrator probes.

With either system, quality control standards are implemented. Normalized values for each probe across three data sets for each indication are averaged. Probes with an average CV % higher than 20% are not used for analysis. Results are subjected to a paired t-test to find differentially expressed miRs between two sample groups. P-values are corrected with a Benjamini and Hochberg false-discovery rate test. Results are analyzed using GeneSpring software (Agilent Technologies, Inc., Santa Clara, Calif.).

Example 18 MicroRNA Profiles in Vesicles

Vesicles were collected by ultracentrifugation from 22Rv1, LNCaP, Vcap and normal plasma (pooled from 16 donors) as described in Examples 1-3. RNA was extracted using the Exiqon miR isolation kit (Cat. Nos. 300110, 300111). Equals amounts of vesicles (30 μg) were used as determined by BCA assay.

Equal volumes (5 μl) were put into a reverse-transcription reaction for microRNA. The reverse-transcriptase reactions were diluted in 81 μl of nuclease-free water and then 9 μl of this solution was added to each individual miR assay. MiR-629 was found to only be expressed in PCa (prostate cancer) vesicles and was virtually undetectable in normal plasma vesicles. MiR-9 was found to be highly overexpressed (˜704 fold increase over normal as measured by copy number) in all PCa cell lines, and has very low expression in normal plasma vesicles. The top ten differentially expressed miRNAs are shown in FIG. 76.

Example 19 MicroRNA Profiles of Magnetic EpCam-Captured Vesicles

The bead-bound vesicles of Example 10 were placed in QIAzol™ Lysis Reagent (Qiagen Cat. #79306). An aliquot of 125 fmol of c. elegans miR-39 was added. The RNA was isolated using the Qiagen miRneasy™ kit, (Cat. #217061), according to the manufacturer's instructions, and eluted in 30 ul RNAse free water.

10 μl of the purified RNA was placed into a pre-amplification reaction for miR-9, miR-141 and miR-629 using a Veriti 96-well thermocycler. A 1:5 dilution of the pre-amplification solution was used to set up a qRT-PCR reaction for miR9 (ABI 4373285), miR-141 (ABI 4373137) and miR-629 (ABI 4380969) as well as c. elegans miR-39 (ABI 4373455). The results were normalized to the c. elegans results for each sample.

Example 20 MicroRNA Profiles of CD9-Captured Vesicles

CD9 coated Dynal beads (Invitrogen, Carlsbad, Calif.) were used instead of EpCam coated beads as in Example 19. Vesicles from prostate cancer patients, LNCaP, or normal purified vesicles were incubated with the CD9 coated beads and the RNA isolated as described in Example 19. The expression of miR-21 and miR-141 was detected by qRT-PCR and the results depicted in FIGS. 77 and 78.

Example 21 Isolation of Vesicles Using a Filtration Module

Six mL of PBS is added to 1 mL of plasma. The sample is then put through a 1.2 micron (μm) Pall syringe filter directly into a 100 kDa MWCO (Millipore, Billerica, Mass.), 7 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.), 15 ml column with a 100 kDa MWCO (Millipore, Billerica, Mass.), or 20 ml column with a 150 kDa MWCO (Pierce®, Rockford, Ill.).

The tube is centrifuged for between 60 to 90 minutes until the volume is about 250 μl. The retentate is collected and PBC added to bring the sample up to 300 μl. Fifty ul of the sample is then used for further vesicle analysis, such as further described in the examples below.

Example 22 Multiplex Analysis of Vesicles Isolated with Filters

The vesicle samples obtained using methods as described in Example 21 are used in multiplexing assays as described herein. See, e.g., Examples 31-33. The capture antibodies are CD9, CD63, CD81, PSMA, PCSA, B7H3, and EpCam. The detection antibodies are for biomarkers CD9, CD81, and CD63 or B7H3 and EpCam, as depicted in FIGS. 79, 80, and 81.

Example 23 Vesicle Isolation with Filters from Patient Samples

Vesicle samples obtained using methods as described in Example 21, using a 7 mL Pierce® concentrator with a 150 kDa MWCO (Cat. #89920/89922) and are used in multiplexing assays as described in herein. See, e.g., Examples 31-33. The capture antibodies are CD9, CD63, CD81, PSMA, PCSA, B7H3, and EpCam. The detection antibodies used are CD63, CD9, and CD81. The results are shown in FIG. 82.

Example 24 Comparison of Vesicles Isolated with Filters Versus with Ultracentrifugation

The vesicle samples obtained using methods as described in Example 21, using a 500 μl column with a 100 kDa MWCO and are used in multiplexing assays as described herein. See, e.g., Examples 31-33. The capture antibodies are CD9, CD63, CD81, PSMA, PCSA, B7H3, and EpCam. The detection antibodies are CD63, CD9, and CD81. The results are shown in FIGS. 83 and 84. Each figure shows different analysis methods performed using samples from a single patient. In both figures, the graphs depict A) ultracentrifugation purified sample; B) filtered sample C) ultracentrifugation purified sample and 10 ug Vcap and D) filtered sample with 10 ug Vcap.

Example 25 Sample Filter Comparison

A variety of filters can be used to remove large debris from the plasma sample as described. Filters ranging in size from 1.2 to 0.8 micron filters provide comparable results, as shown in Tables 21 and 22:

TABLE 21 Filters tested Vendor Cat No. Membrane Pore Size Pall 4190 1.2 μM Millipore SLAA033SB 0.8 μM Millipore SLAA033SS 0.8 μM GVS FJ25ANCCA012CC01 1.2 μM Whatman 6822-1312 GF/C glass fiber 1.2 μM (13 mm) Whatman 6750-2510 Nylon 1.0 μM Whatman 6781-2510 PES 1.0 μM Whatman 10 462 261 Cellulose Acetate 1.2 μM (30 mm) Whatman 6783-2510 GD glass fiber 1.0 μM

Plasma samples were filtered and vesicles detected using the biomarkers CD9, PSMA, PCSA, CD63, CD81, B7H3 and EpCam, as indicated in Table 22. Methodology was as described herein. See, e.g., Examples 31-33. Samples were performed in duplicate. The results for the various filters were comparable within each marker.

TABLE 22 MFI using various filters to isolate vesicles CD9 PSMA PCSA CD63 CD81 B7-H3 EpCam High 28303 23417 22815 28630 28513 24045 27389 Blank 23 10 9 216 7 8 18 Pall 4190 726 15 152 1562 2250 172 94 Pall 4190 787 18 173 1701 2539 208 100 GVS 631 17 163 1722 2738 182 86 GVS 562 19 164 1504 2315 206 95 SLAA033SB 739 26 243 1521 2078 277 135 SLAA033SB 725 23 160 1316 1790 263 116 SLAA033SS 888 19 191 1384 2025 223 124 SLAA033SS 774 22 173 1392 2063 218 100 6822-1312 824 19 196 1601 2359 207 106 6822-1312 697 18 172 1632 2580 202 93 10 462 261 576 19 178 1495 2420 190 93 10 462 261 553 23 191 1465 2091 221 106 6750-2510 743 32 248 1574 2082 286 144 6750-2510 847 38 250 1545 2019 322 171 6783-2510 853 19 178 1474 2185 219 110 6783-2510 757 20 159 1533 2232 199 103 6781-2510 624 23 202 1433 2189 227 111 6781-2510 711 21 196 1365 1966 218 131

Example 26 Flow Cytometry Analysis of Vesicles

Purified plasma vesicles are assayed using the MoFlo XDP (Beckman Coulter, Fort Collins, Colo., USA) and the median fluorescent intensity analyzed using the Summit 4.3 Software (Beckman Coulter). Vesicles are labeled directly with antibodies, or beads or microspheres (e.g., magnetic, polystyrene, including BD FACS 7-color setup, catalog no. 335775) can be incorporated. Vesicles can be detected with binding agents against the following vesicle antigens: CD9 (Mouse anti-human CD9, MAB1880, R&D Systems, Minneapolis, Minn., USA), PSM (Mouse anti-human PSM, sc-73651, Santa Cruz, Santa Cruz, Calif., USA), PCSA (Mouse anti-human Prostate Cell Surface Antigen, MAB4089, Millipore, Mass., USA), CD63 (Mouse anti-human CD63, 556019, BD Biosciences, San Jose, Calif., USA), CD81 (Mouse anti-human CD81, 555675, BD Biosciences, San Jose, Calif., USA) B7-H3 (Goat anti-human B7-H3, AF1027, R&D Systems, Minneapolis, Minn., USA), EpCAM (Mouse anti-human EpCAM, MAB9601, R&D Systems, Minneapolis, Minn., USA). Vesicles can be detected with fluorescently labeled antibodies against the desired vesicle antigens. For example, FITC, phycoerythrin (PE) and Cy7 are commonly used to label the antibodies.

To capture the antibodies with multiplex microspheres, the microspheres can be obtained from Luminex (Austin, Tex., USA) and conjugated to the desired antibodies using micros using Sulfo-NHS and EDC obtained from Pierce Thermo (Cat. No. 24510 and 22981, respectively, Rockford, Ill., USA).

Purified vesicles (10 ug/ml) are incubated with 5,000 microspheres for one hour at room temperature with shaking. The samples are washed in FACS buffer (0.5% FBS/PBS) for 10 minutes at 1700 rpms. The detection antibodies are incubated at the manufacturer's recommended concentrations for one hour at room temperature with shaking. Following another wash with FACS buffer for 10 minutes at 1700 rpms, the samples are resuspended in 100 ul FACS buffer and run on the FACS machine.

Further when using microspheres to detect vesicles, the labeled vesicles can be sorted according to their detection antibody content into different tubes. For example, using FITC or PE labeled microspheres, a first tube contains the population of microspheres with no detectors, the second tube contains the population with PE detectors, the third tube contains the population with FITC detectors, and the fourth tube contains the population with both PE and FITC detectors. The sorted vesicle populations can be further analyzed, e.g., by examining payload such as mRNA, microRNA or protein content.

FIG. 85 shows separation and identification of vesicles using the MoFlo XDP. In this set of experiments, there were about 3000 trigger events with just buffer (i.e. particulates about the size of a large vesicle). There were about 46,000 trigger events with unstained vesicles (43,000 vesicles of sufficient size to scatter the laser). There were 500,000 trigger events with stained vesicles. Vesicles were detected using detection agents for tetraspanins CD9, CD63, and CD81 all labeled with FITC. The smaller vesicles can be detected when they are stained with detection agents.

FIG. 86A shows flow sorting of vesicles from the plasma of a prostate cancer patient using Cy7-labeled anti-PSCA antibodies. The percentage of PCSA+ vesicles increased from 35% to 68% upon flow sorting. FIG. 86B shows flow sorting of vesicles from the plasma of a normal patient and a prostate cancer patient using labeled anti-CD45 antibodies. There is approximately a five-fold greater percentage of CD45+ vesicles in the cancer plasma compared with the healthy control. The percentage of CD45+ vesicles was greatly increased after flow sorting. As CD45 is an immune response marker, the increased immune derived vesicles demonstrate an immune response in the prostate cancer patient. FIG. 86C shows flow sorting of vesicles from the plasma of a normal patient and a breast cancer patient using labeled anti-CD45 antibodies. There are more than a 10-fold greater percentage of CD45+ vesicles in the breast cancer plasma compared with the healthy control. The percentage of CD45+ vesicles was greatly increased after sorting. As CD45 is an immune response marker, the increased immune derived vesicles demonstrate an immune response in the breast cancer patient. FIG. 86D shows flow sorting of vesicles from the plasma of a normal patient and a prostate cancer patient using labeled anti-DLL4 antibodies. There are approximately a 10-fold greater percentage of DLL4+ vesicles in the prostate cancer plasma compared with the healthy control. The percentage of DLL4+ vesicles was greatly increased by flow sorting. Increased DLL4+ vesicles may indicate increased angiogenenesis in the cancer patients.

Physical isolation by sorting of specific populations of vesicles facilitates additional studies such as microRNA analysis on the partially or wholly purified vesicle populations.

Example 27 Antibody Detection of Vesicles

Vesicles in a patient sample are assessed using antibody-coated beads to detect the vesicles in the sample using techniques as described herein. The following general protocol is used:

-   -   a. Blood is drawn from a patient at a point of care (e.g.,         clinic, doctor's office, hospital).     -   b. The plasma fraction of the blood is used for further         analysis.     -   c. To remove large particles and isolate a vesicle containing         fraction, the plasma sample is filtered, e.g., with a 0.8 or 1.2         micron (μm) syringe filter, and then passed through a size         exclusion column, e.g., with a 150 kDa molecular weight cut off.         A general schematic is shown in FIG. 87A. Filtration may be         preferable to ultracentrifugation, as illustrated in FIG. 87B.         Without being bound by theory, high-speed centrifugation may         remove protein targets weakly anchored in the membrane as         opposed to the tetraspanins which are more solidly anchored in         the membrane, and may reduce the cell specific targets in the         vesicle, which would then not be detected in subsequent analysis         of the biosignature of the vesicle.     -   d. The vesicle fraction is incubated with beads conjugated with         a “capture” antibody to a marker of interest. The captured         vesicles are then tagged with labeled “detection” antibodies,         e.g., phycoerythrin or FITC conjugated antibodies. The beads can         be labeled as well.     -   e. Captured and tagged vesicles in the sample are detected.         Fluorescently labeled beads and detection antibodies can be         detected as shown in FIG. 87C. Use of the labeled beads and         labeled detection antibodies allows assessment of beads with         vesicles bound thereto by the capture antibody.     -   f. Data is analyzed. A threshold can be set for the median         fluorescent intensity (MFI) of a particular capture antibody. A         reading for that capture antibody above the threshold can         indicate a certain phenotype. As an illustrative example, an MFI         above the threshold for a capture antibody directed to a cancer         marker can indicate the presense of cancer in the patient         sample.

In FIG. 87, the beads 816 flow through a capillary 811. Use of dual lasers 812 at different wavelengths allows separate detection at detector 813 of both the capture antibody 818 from the fluorescent signal derived from the bead, as well as the median fluorescent intensity (MFI) resulting from the labeled detection antibodies 819. Use of labeled beads conjugated to different capture antibodies of interest, each bead labeled with a different fluor, allows for mulitiplex analysis of different vesicle 817 populations in a single assay as shown. Laser 1 815 allows detection of bead type (i.e., the capture antibody) and Laser 2 814 allows measurement of detector antibodies, which can include general vesicle markers such as tetraspanins including CD9, CD63 and CD81. Use of different populations of beads and lasers allows simultaneous multiplex analysis of many different populations of vesicles in a single assay.

Example 28 Vesicle Reference Values for Prostate Cancer

Fourteen stage 3 prostate cancer subjects, eleven benign prostate hyperplasia (BPH) samples, and 15 normal samples were tested. Vesicle samples were obtained using methods as described in Example 3 and used in multiplexing assays as described in Examples 4 and 5. The samples were analyzed to determine four criteria 1) if the sample has overexpressed vesicles, 2) if the sample has overexpressed prostate vesicles, 3) if the sample has overexpressed cancer vesicles, and 4) if the sample is reliable. If the sample met all four criteria, the categorization of the sample as positive for prostate cancer had varying sensitivities and specificities, depending on the different biosignatures present for a sample as shown in Table 23.

In the table, “Vesicle” lists the threshold value or reference value of vesicle levels, “Prostate” lists the threshold value or reference value used for prostate vesicles, “Cancer-1,” “Cancer-2,” and “Cancer-3” lists the threshold values or reference values for the three different biosignatures for prostate cancer, the “QC-1” and “QC-2” columns list the threshold values or reference values for quality control, or reliability, and the last four columns list the specificities (“Spec”) and sensitivities (“Sens”) for benign prostate hyperplasia (BPH).

TABLE 23 Sensitivity and Specificity of Prostate Cancer Biosignatures Sens Spec Sens Spec With With Without Without Vesicle Prostate Cancer-1 Cancer-2 Cancer-3 QC-1 QC-2 BPH BPH BPH BPH 3000 100 na 200 n/a 4000 n/a 85.70% 58.00% 85.70% 71.40% 3000 100 350 100 n/a 4000 n/a 85.70% 74.10% 85.70% 85.70% 3000 100 125 125 50 4000 n/a 71.40% 83.00% 71.40% 90.40% 3000 100 100 100 50 4000 8000 71.40% 87.00% 71.40% 90.40% 3000 100 100 150 50 4000 n/a 64.30% 90.30% 64.20% 90.40% 3000 100 100 150 150 4000 n/a 35.70% 93.40% 35.70% 95.20%

The four criteria used to categorize the samples were as follows:

Vesicle Overexpression

The median fluorescence intensities (MFIs) for a sample in three assays was used to determine a value for the sample. Each assay used a different capture antibody. The first used a CD9 capture antibody, the second a CD81 capture antibody, and the third a CD63 antibody. The same combination of detection antibodies was used for each assay, antibodies for CD9, CD81, and CD63. If the average value obtained for the three assays was greater than 3000, the sample was categorized as having overexpressed vesicles (Table 23, Vesicle).

Prostate Vesicle Overexpression

The MFIs for a sample in two assays were averaged to determine a value for the sample. Each assay used a different capture antibody. The first used a PCSA capture antibody and the second used a PSMA capture antibody. The same combination of labeled detection antibodies to CD9, CD81, and CD63 was used for each assay. If the average value obtained for the two assays was greater than 100, the sample was categorized as having prostate vesicles overexpressed (Table 23, Prostate).

Cancer Vesicle Overexpression

Three different cancer biosignatures were used to determine if cancer vesicles were overexpressed in a sample. The first, Cancer-1, used an EpCam capture antibody and detection antibodies for CD81, CD9, and CD63. The second, Cancer-2, used a CD9 capture antibody with detection antibodies for EpCam and B7H3. If the MFI value of a sample for any two of the three cancer biosignatures was above a reference value, the sample was categorized as having overexpressed cancer (see Table 23, Cancer-1, Cancer-2, Cancer-3).

Reliability of Sample

Two quality control measures, QC-1 and QC-2, were determined for each sample. If the sample met one of them, the sample was categorized as reliable.

For QC-1, the sum of all the MFIs of 7 assays was determined. Each of the 7 assays used detection antibodies for CD59 and PSMA. The capture antibody used for each assay was CD63, CD81, PCSA, PSMA, STEAP, B7H3, and EpCam. If the sum was greater than 4000, the sample was not reliable and not included.

For QC-2, the sum of all the MFIs of 5 assays was determined. Each of the 5 assays used detection antibodies for CD9, CD81 and CD63. The capture antibody used for each assay was PCSA, PSMA, STEAP, B7H3, and EpCam. If the sum was greater than 8000, the sample was not reliable and not included.

The sensitivity and specificity for samples with BPH and without BPH samples after a sample met the criteria are shown in Table 23.

Example 29 Detection of Prostate Cancer

High quality training set samples were obtained from commercial suppliers. The samples comprised plasma from 42 normal prostate, 42 PCa and 15 BPH patients. The PCa samples included 4 stage III and the remainder state II. The samples were blinded until all laboratory work was completed.

The vesicles from the samples were obtained by filtration to eliminate particles greater than 1.5 microns, followed by column concentration and purification using hollow fiber membrane tubes. The samples were analyzed using a multiplexed bead-based assay system as described above.

Antibodies to the following proteins were analyzed:

-   -   a. General Vesicle (MV) markers: CD9, CD81, and CD63     -   b. Prostate MV markers: PCSA     -   c. Cancer-Associated MV markers: EpCam and B7H3

Samples were required to pass a quality test as follows: if multiplexed median fluorescence intensity (MFI) PSCA+MFI B7H3+MFI EpCam <200 then sample fails due to lack of signal above background. In the training set, six samples (three normals and three prostate cancers) did not achieve an adequate quality score and were excluded. An upper limit on the MFI was also established as follows: if MFI of EpCam is >6300 then test is over the upper limit score and samples are deemed not cancer (i.e., “negative” for purposes of the test).

The samples were classified according to the result of MFI scores for the six antibodies to the training set proteins, wherein the following conditions must be met for the sample to be classified as PCa positive:

-   -   a. Average MFI of General MV markers >1500     -   b. PCSA MFI >300     -   c. B7H3 MFI >550     -   d. EpCam MFI between 550 and 6300

Using the 84 normal and PCa training data samples, the test was found to be 98% sensitive and 95% specific for PCa vs normal samples. See FIG. 88A. The increased MFI of the PCa samples compared to normals is shown in FIG. 88B. The sensitivity and specificity of the test compared to conventional PSA and PCA3 are presented in FIGS. 89A and 89B, respectively. Compared to PSA and PCA3 testing, the PCa Test presented in this Example can result in saving 220 men without PCa in every 1000 normal men screened from having an unnecessary biopsy.

Example 30 Differentiating BPH from PCa

BPH is a common cause of elevated PSA levels. PSA can only indicated whether there is something wrong with the prostate, but it cannot effectively differentiate between BPH and PCa. PCA3, a transcript found to be overexpressed by prostate cancer cells, is thought to be slightly more specific for PCa, but this depends on the cutoffs used for PSA and PCA3, as well as the populations studied.

BPH can be characterized by vesicle (MV) analysis. Examining the samples described in Example 29, ten out of the 15 BPH samples (67%) have higher levels of CD63+ vesicles than the PCa samples, including the stage IIIs. See FIG. 90. Also, 14 out of 15 BPH (93%) have higher levels of CD63+ vesicles than the normals. This indicates that an inflammation-specific signature that differs from cancer may be used in differentiating BPH from PCa.

The PCa test as in Example 29 was repeated including the 15 BPH samples. Using all 99 samples, the test was 98% sensitive and 84% specific. See FIG. 91. Thus, the test provides a 15% improvement over PSA. Performance values for PSA and PCA3 are commonly reported for settings without BPH in their cohorts, nevertheless, the vesicle test of the invention still outperforms conventional testing even when BPH was included. See FIG. 92. In this setting, the PCa test of the invention results in saving 110 men in every 1000 men without PCa screened from having an unnecessary biopsy as compared to PSA testing. And of those men biopsied due to a positive result from the assay, most will have something wrong with their prostate because the test performs well at identifying normal men (i.e., 95% specific in that population, see Example 29).

FIG. 93 presents ROC curve analysis of the vesicle assays of the invention versus conventional testing. When the ROC curve climbs rapidly towards upper left hand corner of the graph, the true positive rate is high and the false positive rate (1—specificity) is low. The AUC comparison shown in FIG. 93 shows that the test of the invention is much more likely to correctly classify a sample than conventional PSA or PCA3 testing.

FIG. 94 shows that there is a correlation between general vesicle (MV) levels, levels of prostate-specific MVs and MVs with cancer markers, indicating these markers are correlated in the subject populations. Such cancer specific markers can be further used to differentiate between BPH and PCa. In the figure, General MV markers include CD9, CD63 and CD81; Prostate MV markers include PCSA and PSMA; and Cancer MV markers include EpCam and B7H3. Testing of PCa samples without the vesicle capture markers revealed sensitivity and specificity values nearly the same as those with the general MV markers were used. Similarly, detection of cancer without using B7H3 only leads to minimal reduction in performance. These data reveal that the markers of the invention can be substituted and tested in various configurations to still achieve optimal assay performance.

FIG. 95 shows additional markers that can distinguish between PCa and normal samples that can be added to improve test performance. The figure shows the median fluorescence intensity (MFI) levels of vesicles captured with ICAM1, EGFR, STEAP1 and PSCA and labeled with phycoerythrin-labeled antibodies to tetraspanins CD9, CD63 and CD81.

Example 31 Microsphere Vesicle Prostate Cancer Assay Protocol

In this example, the vesicle PCa test is a microsphere based immunoassay for the detection of a set of protein biomarkers present on the vesicles from plasma of patients with prostate cancer. The test employs specific antibodies to the following protein biomarkers: CD9, CD59, CD63, CD81, PSMA, PCSA, B7H3 and EpCAM. After capture of the vesicles by antibody coated microspheres, phycoerythrin-labeled antibodies are used for the detection of vesicle specific biomarkers. See FIG. 96A. Depending on the level of binding of these antibodies to the vesicles from a patient's plasma a determination of the presence or absence of prostate cancer is made.

Vesicles are isolated as described in Example 7.

Microspheres

Specific antibodies are conjugated to microspheres (Luminex) after which the microspheres are combined to make a Microsphere Master Mix consisting of L100-C105-01; L100-C115-01; L100-C119-01; L100-C120-01; L100-C122-01; L100-C124-01; L100-C135-01; and L100-C175-01. xMAP® Classification Calibration Microspheres L100-CALL (Luminex) are used as instrument calibration reagents for the Luminex LX200 instrument. xMAP® Reporter Calibration Microspheres L100-CAL2 (Luminex) are used as instrument reporter calibration reagents for the Luminex LX200 instrument. xMAP® Classification Control Microspheres L100-CON1 (Luminex) are used as instrument control reagents for the Luminex LX200 instrument. xMAP Reporter Control Microspheres L100-CON2 (Luminex) and are used as reporter control reagents for the Luminex LX200 instrument.

Capture Antibodies

The following antibodies are used to coat Luminex microspheres for use in capturing certain populations of vesicles by binding to their respective protein targets on the vesicles in this Example: a. Mouse anti-human CD9 monoclonal antibody is an IgG2b used to coat microsphere L100-C105 to make *EPCLMACD9-C105; b. Mouse anti-human PSMA monoclonal antibody is an IgG1 used to coat microsphere L100-C115 to make EPCLMAPSMA-C115; c. Mouse anti-human PCSA monoclonal antibody is an IgG1 used to coat microsphere L100-C119 to make EPCLMAPCSA-C119; d. Mouse anti-human CD63monoclonal antibody is an IgG1 used to coat microsphere L100-C120 to make EPCLMACD63-C120; e. Mouse anti-human CD81 monoclonal antibody is an IgG1 used to coat microsphere L100-C124 to make EPCLMACD81-C124; f. Goat anti-human B7-H3 polyclonal antibody is an IgG purified antibody used to coat microsphere L100-C125 to make EPCLGAB7-H3-C125; and g. Mouse anti-human EpCAM monoclonal antibody is an IgG2b purified antibody used to coat microsphere L100-C175 to make EPCLMAEpCAM-C175.

Detection Antibodies

The following phycoerythrin (PE) labeled antibodies are used as detection probes in this assay: a. EPCLMACD81PE: Mouse anti-human CD81 PE labeled antibody is an IgG1 antibody used to detect CD81 on captured vesicles; b. EPCLMACD9PE: Mouse anti-human CD9 PE labeled antibody is an IgG1 antibody used to detect CD9 on captured vesicles; c. EPCLMACD63PE: Mouse anti-human CD63 PE labeled antibody is an IgG1 antibody used to detect CD63 on captured vesicles; d. EPCLMAEpCAMPE: Mouse anti-human EpCAM PE labeled antibody is an IgG1 antibody used to detect EpCAM on captured vesicles; e. EPCLMAPSMAPE: Mouse anti-human PSMA PE labeled antibody is an IgG1 antibody used to detect PSMA on captured vesicles; f. EPCLMACD59PE: Mouse anti-human CD59 PE labeled antibody is an IgG1 antibody used to detect CD59 on captured vesicles; and g. EPCLMAB7-H3PE: Mouse anti-human B7-H3 PE labeled antibody is an IgG1 antibody used to detect B7-H3 on captured vesicles.

Reagent Preparation

Antibody Purification:

The following antibodies in Table 24 are received from vendors and purified and adjusted to the desired working concentrations according to the following protocol.

TABLE 24 Antibodys for PCa Assay Antibody Use EPCLMACD9 Coating of microspheres for vesicle capture EPCLMACD63 Coating of microspheres for vesicle capture EPCLMACD81 Coating of microspheres for vesicle capture EPCLMAPSMA Coating of microspheres for vesicle capture EPCLGAB7-H3 Coating of microspheres for vesicle capture EPCLMAEpCAM Coating of microspheres for vesicle capture EPCLMAPCSA Coating of microspheres for vesicle capture EPCLMACD81PE PE coated antibody for vesicle biomarker detection EPCLMACD9PE PE coated antibody for vesicle biomarker detection EPCLMACD63PE PE coated antibody for vesicle biomarker detection EPCLMAEpCAMPE PE coated antibody for vesicle biomarker detection EPCLMAPSMAPE PE coated antibody for vesicle biomarker detection EPCLMACD59PE PE coated antibody for vesicle biomarker detection EPCLMAB7-H3PE PE coated antibody for vesicle biomarker detection

Antibody Purification Protocol: Antibodies are purified using Protein G resin from Pierce (Protein G spin kit, prod #89979). Micro-chromatography columns made from filtered P-200 tips are used for purification.

One hundred μl of Protein G resin is loaded with 100 μl buffer from the Pierce kit to each micro column. After waiting a few minutes to allow the resin to settle down, air pressure is applied with a P-200 Pipettman to drain buffer when needed, ensuring the column is not let to dry. The column is equilibrated with 0.6 ml of Binding Buffer (pH 7.4, 100 mM Phosphate Buffer, 150 mM NaCl; (Pierce, Prod #89979). An antibody is applied to the column (<1 mg of antibody is loaded on the column). The column is washed with 1.5 ml of Binding Buffer. Five tubes (1.5 ml micro centrifuge tubes) are prepared and 10 μl of neutralization solution (Pierce, Prod #89979) is applied to each tube. The antibody is eluted with the elution buffer from the kit to each of the five tubes, 100 ul for each tube (for a total of 500 μl). The relative absorbance of each fraction is measured at 280 nm using Nanodrop (Thermo scientific, Nanodrop 1000 spectrophotometer). The fractions with highest OD reading are selected for downstream usage. The samples are dialyzed against 0.25 liters PBS buffer using Pierce Slide-A-Lyzer Dialysis Cassette (Pierce, prod 66333, 3 KDa cut off). The buffer is exchanged every 2 hours for minimum three exchanges at 4° C. with continuous stirring. The dialyzed samples are then transferred to 1.5 ml microcentifuge tubes, and can be labeled and stored at 4° C. (short term) or −20° C. (long term).

Microsphere Working Mix Assembly:

A microsphere working mix MWM101 includes the first four rows of antibody, microsphere and coated microsphere of Table 25.

TABLE 25 Antibody-Microsphere Combinations Antibody Microsphere Coated Microsphere EPCLMACD9 L100-C105 EPCLMACD9-C105 EPCLMACD63 L100-C120 EPCLMACD63-C120 EPCLMACD81 L100-C124 EPCLMACD81-C124 EPCLMAPSMA L100-C115 EPCLMAPSMA-C115 EPCLGAB7-H3 L100-C125 EPCLGAB7-H3-C125 bEPCLMAEpCAM L100-C175 EPCLMAEpCAM-C175 EPCLMAPCSA L100-C119 EPCLMAPCSA-C119

Microspheres are coated with their respective antibodies as listed above according to the following protocol.

Protocol for Two-Step Carbodiimide Coupling of Protein to Carboxylated Microspheres:

The microspheres should be protected from prolonged exposure to light throughout this procedure. The stock uncoupled microspheres are resuspended according to the instructions described in the Product Information Sheet provided with the microspheres (xMAP technologies, MicroPlex™ Microspheres). Five ×106 of the stock microspheres are transferred to a USA Scientific 1.5 ml microcentrifuge tube. The stock microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature. The supernatant is removed and the pelleted microspheres are resuspended in 100 μl of dH2O by vortex and sonication for approximately 20 seconds. The microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature. The supernatant is removed and the washed microspheres are resuspended in 80 μl of 100 mM Monobasic Sodium Phosphate, pH 6.2 by vortex and sonication (Branson 1510, Branson UL Trasonics Corp.) for approximately 20 seconds. Ten μl of 50 mg/ml Sulfo-NHS (Thermo Scientific, Cat#24500) (diluted in dH2O) is added to the microspheres and is mixed gently by vortex. Ten μl of 50 mg/ml EDC (Thermo Scientific, Cat#25952-53-8) (diluted in dH2O) is added to the microspheres and gently mixed by vortexing. The microspheres are incubated for 20 minutes at room temperature with gentle mixing by vortex at 10 minute intervals. The activated microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature. The supernatant is removed and the microspheres are resuspended in 250 μl of 50 mM MES, pH 5.0 (MES, Sigma, Cat# M2933) by vortex and sonication for approximately 20 seconds. (Only PBS-1% BSA+Azide (PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) should be used as assay buffer as well as wash buffer.). The microspheres are then pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature.

The supernatant is removed and the microspheres are resuspended in 250 μl of 50 mM MES, pH 5.0 (MES, Sigma, Cat# M2933) by vortex and sonication for approximately 20 seconds. (Only PBS-1% BSA+Azide (PBS-BN) ((Sigma (P3688-10PAK+0.05% NaAzide (S8032))) should be used as assay buffer as well as wash buffer.). The microspheres are then pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature, thus completing two washes with 50 mM MES, pH 5.0.

The supernatant is removed and the activated and washed microspheres are resuspended in 100 μl of 50 mM MES, pH 5.0 by vortex and sonication for approximately 20 seconds. Protien in the amount of 125, 25, 5 or 1 μg is added to the resuspended microspheres. (Note: Titration in the 1 to 125 μg range can be performed to determine the optimal amount of protein per specific coupling reaction.). The total volume is brought up to 500 μl with 50 mM MES, pH 5.0. The coupling reaction is mixed by vortex and is incubated for 2 hours with mixing (by rotating on Labquake rotator, Barnstead) at room temperature. The coupled microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature. The supernatant is removed and the pelleted microspheres are resuspended in 500 μL of PBS-TBN by vortex and sonication for approximately 20 seconds. (Concentrations can be optimized for specific reagents, assay conditions, level of multiplexing, etc. in use.).

The microspheres are incubated for 30 minutes with mixing (by rotating on Labquake rotator, Barnstead) at room temperature. The coupled microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes at room temperature. The supernatant is removed and the microspheres are resuspended in 1 ml of PBS-TBN by vortex and sonication for approximately 20 seconds. (Each time there is the addition of samples, detector antibody or SA-PE the plate is covered with a sealer and light blocker (such as aluminum foil), placed on the orbital shaker and set to 900 for 15-30 seconds to re-suspend the beads. Following that the speed should be set to 550 for the duration of the incubation.).

The microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes. The supernatant is removed and the microspheres are resuspended in 1 ml of PBS-TBN by vortex and sonication for approximately 20 seconds. The microspheres are pelleted by microcentrifugation at ≧8000×g for 1-2 minutes (resulting in a total of two washes with 1 ml PBS-TBN).

Protocol for Microsphere Assay:

The preparation for multiple phycoerythrin detector antibodies is used as described in Example 4. One hundred μl is analyzed on the Luminex analyzer (Luminex 200, xMAP technologies) according to the system manual (High PMT setting).

Decision Tree:

A decision tree (FIG. 96B-D) is used to assess the results from the microsphere assay to determine if a subject has cancer. Threshold limits on the MFI is established and samples classified according to the result of MFI scores for the antibodies, to determine whether a sample has sufficient signal to perform analysis (e.g., is a valid sample for analysis or an invalid sample for further analysis, in which case a second patient sample may be obtained) and whether the sample is PCa positive. FIG. 96B shows a decision tree using the MFI obtained with CD59, PSMA, PCSA, B7-H3, EpCAM, CD9, CD81 and CD63. FIG. 96C shows a decision tree using the MFI obtained with PSMA, B7-H3, EpCAM, CD9, CD81 and CD63. A sample is classified as indeterminate if the MFI is within the standard deviation of the predetermined threshold. For validation, the sample must have sufficient signal when capturing vesicles with the individual tetraspanins and labeling with all tetraspanins. A sample that passes validation is called positive if the prostate-specific marker (PSMA) is considered positive and the joint signal of the cancer markers (B7-H3 and EpCam) is also considered positive. FIG. 96D shows a decision tree using the MFI obtained with PCSA, PSMA, B7-H3, CD9, CD81 and CD63. A sample is classified as indeterminate if the MFI is within the standard deviation of the predetermined threshold (TH). In this case, a second patient sample can be obtained. For validation, the sample must have sufficient signal when capturing vesicles with the individual tetraspanins and labeling with all tetraspanins. A sample that passes validation is called positive if either of the prostate-specific markers (PSMA or PCSA) is considered positive, and the cancer marker (B7-H3) is also considered positive.

Results:

See Examples 32 and 33.

Example 32 Microsphere Vesicle PCa Assay Performance

In this example, the vesicle PCa test is a microsphere based immunoassay for the detection of a set of protein biomarkers present on the vesicles from plasma of patients with prostate cancer. The test is performed similarly to that of Example 31 with modifications indicated below.

The test uses a multiplexed immunoassay designed to detect circulating microvesicles. The test uses PCSA, PSMA and B7H3 to capture the microvesicles present in patient samples such as plasma and uses CD9, CD81, and CD63 to detect the captured microvesicles. The output of this assay is the median fluorescent intensity (MFI) that results from the antibody capture and fluorescently labeled antibody detection of microvesicles that contain both the individual capture protein and the detector proteins on the microvesicle. A sample is “POSITIVE” by this test if the MFI levels of PSMA or PCSA, and B7H3 protein-containing microvesicles are above the empirically determined threshold. A sample is determined to be “NEGATIVE” if any one of these two microvesicle capture categories exhibit an MFI level that is below the empirically determined threshold. Alternatively, a result of “INDETERMINATE” will be reported if the sample MFI fails to clearly produce a positive or negative result due to MFI values not meeting certain thresholds or the replicate data showed too much statistical variation. A “NON-EVALUABLE” interpretation for this test indicates that this patient sample contained inadequate microvesicle quality for analysis. See Example 33 for a method to determine the empirically derived threshold values.

The test employs specific antibodies to the following protein biomarkers: CD9, CD59, CD63, CD81, PSMA, PCSA, and B7H3 as in Example 31. Decision rules are set to determine if a sample is called positive, negative or indeterminate, as outlined in Table 26. See also Example 31. For a sample to be called positive the replicates must exceed all four of the MFI cutoffs determined for the tetraspanin markers (CD9, CD63, CD81), prostate markers (PSMA or PCSA), and B7H3. Samples are called indeterminate if both of the three replicates from PSMA and PCSA or any of the three replicates from B7H3 antibodies span the cutoff MFI value. Samples are called negative if there is at least one of the tetraspanin markers (CD9, CD63, and CD81), prostate markers (PSMA or PCSA), B7H3 that fall below the MFI cutoffs.

TABLE 26 MFI Parameter for Each Capture Antibody Tetraspanin Markers Prostate Markers Result (CD9, CD63, CD81) (PSMA, PCSA) B7H3 Determination Average of all All replicates from All replicates from If all 3 are true, replicates from the either of the two B7H3 have a then the sample is three tetraspanins have prostate markers have MFI >300 called Positive a MFI >500 a MFI >350 for PCSA and >90 for PSMA Both replicate sets Any replicates If either are true, from either prostate from B7H3 have then the sample is marker have values values both above called both above and below and below a indeterminate a MFI =350 for PCSA MFI = 300 and =90 for PSMA All replicates from the All replicates from All replicates from If any of the 3 are three tetraspanins have either of the two B7H3 have a true, then the a MFI <500 prostate markers have MFI <300 sample is called a MFI <350 for PCSA Negative, given the and <90 for PSMA sample doesn't qualify as indeterminate

The vesicle PCa test was compared to elevated PSA on a cohort of 296 patients with or without PCa as confirmed by biopsy. An ROC curve of the results is shown in FIG. 97A. As shown, the area under the curve (AUC) for the vesicle PCa test was 0.94 whereas the AUC for elevated PSA on the same samples was only 0.68. The PCa samples were likely found due to a high PSA value. Thus this population is skewed in favor of PSA, accounting for the higher AUC than is observed in a true clinical setting.

The vesicle PCa test was further performed on a cohort of 933 patient plasma samples. Results are shown in FIG. 97B and are summarized in Table 27:

TABLE 27 Performance of vesicle PCa test on 933 patient cohort True Positive 409 True Negative 307 False Positive 50 False Negative 72 Non-evaluable 63 Indeterminate 32 Total 933 Sensitivity 85% Specificity 86% Accuracy 85% Non-evaluable Rate  8% Indeterminate Rate  5%

As shown in Table 27, the vesicle PCa test achieved an 85% sensitivity level at a 86% specificity level, for an accuracy of 85%. In contrast, PSA at a sensitivity of 85% had a specificity of about 55%, and PSA at a specificity of 86% had a sensitivity of about 5%. FIG. 97A. About 12% of the 933 samples were non-evaluable or indeterminate. Samples from the patients could be recollected and re-evaluated. FIG. 97C shows an ROC curve corresponding to the data shown in FIG. 97B. The vesicle PCa test had an AUC of 0.92 for the 933 samples.

Example 33 Median Fluorescence Intensity (MFI) Threshold Calculations

It is common to set a threshold level for a biomarker, wherein values above or below the threshold signify differential results, e.g., positive versus negative results. For example, the standard for PSA is a threshold of 4 ng/ml of PSA in serum. PSA levels below this threshold are considered normal whereas PSA values above this threshold may indicate a problem with the prostate, e.g., BPH or prostate cancer (PCa). The threshold can be adjusted to favor enhanced sensitivity versus specificity. In the case of PSA, a lower threshold would detect more cancers, and thus increase sensitivity, but would concomitantly increase the number of false positives, and thus decrease specificity. Similarly, a higher threshold would detect fewer cancers, and thus decrease sensitivity, but would concomitantly decrease the number of false positives, and thus increase specificity.

In the Examples herein such as Examples 29-32, threshold MFI values are set for the vesicle biomarkers to construct a test for detecting PCa. This Example provides an approach to determining the threshold values. To this end, cluster analysis was used to determine if there were PCa positive and negative populations that could be separated based on MFI threshold values that result in the desired level of sensitivity.

Fluorescence intensity values are exponentially distributed, thus prior to performing the clustering analysis, the data was logarithmically transformed. The resulting data set was subjected to traditional hard clustering methods. The hard clustering implemented here uses defined Euclidean distance parameter to determine if a data point belongs to a particular cluster. The algorithm used allocates each data point to one of c clusters to minimize the within-cluster sum of squares:

$\sum\limits_{i = 1}^{c}{\sum\limits_{k \in A_{i}}{{x_{k} - v_{i}}}_{2}}$

where A_(i) is a set of objects (data points) in the i-th cluster and v_(i) is the mean for those points over cluster i. This equation denotes a Euclidian distance norm. The data from 149 samples was used to determine the clusters. The raw data was logarithmically transformed so that it was uniformly distributed. The data was then normalized by subtracting the minimum value and dividing by the maximum. Plots of PSMA vs B7H3, PCSA vs B7H3, and PSMA vs PCSA both before and after transformation are shown in FIG. 98A.

Each possible combination of markers was analyzed, PSMA vs B7H3, PCSA vs PSMA, and PCSA vs B7H3 and thresholds determined to best separate the populations identified. Horizontal and vertical lines where found that best separated the two clusters. The point where the line crossed the axis was used to define the cutoff, which required first that the value be denormalized, then the antilog taken. This resulted in cutoffs of 90 and 300 for each PSMA vs B7H3 respectively, as shown in shown in FIG. 98B.

For PCSA vs B7H3, the two clusters found are shown in FIG. 98C. Horizontal and vertical lines where found that best separated the two clusters. The point that the line crossed the axis was used to define the cutoff, which required first that the value be denormalized, then the antilog taken. This resulted in cutoffs of 430 and 300 for each PCSA vs B7H3 respectively.

For PSMA vs PCSA, the two clusters found are shown in FIG. 98D. Horizontal and vertical lines where found that best separated the two clusters. The point that the line crossed the axis was used to define the cutoff, which required first that the value be denormalized, then the antilog taken. This resulted in in cutoffs of 85 and 350 for each PSMA vs PCSA respectively.

Sensitivity and specificity were calculated for all combinations of thresholds found with the cluster analysis. There was no change in sensitivity or specificity with values of 85 or 90 for PSMA, thus 90 was chosen to use as the cutoff. There was no change in sensitivity with thresholds of 430 or 350 for PCSA, though specificity decreased by 0.3% with the change. Since this is an insignificant change, a value of 350 was chosen for the PCSA cutoff so as to err on the side of higher sensitivity. Both clusters had the same threshold of 300 for B7H3, so this value was used. The resulting sensitivity and specificity with these threshold values was 92.7% and 81.8% respectively.

These thresholds were applied to the larger set of data containing 313 samples, and resulted in a sensitivity of 92.8% and a specificity of 78.7%. See FIG. 98E.

The thresholds in this Example were determined in a fashion that was independent of whether the samples were from normal or cancer patients. Since the thresholds perform well at separating the two populations, it is likely that there are in fact two separate underlying populations due to differences in the biology of the specimens. This difference is highly correlated to the presence or absence of prostate cancer, and thus serves as a good recommendation for the performance of a biopsy. The method is used to determine MFI thresholds for any desired comparison such as detecting other cancers from normal.

Example 34 Vesicle PCa Protein Marker Discovery

In this example, vesicle protein biomarkers were assessed as above. Samples were derived from a total of 522 patients, including 285 prostate cancer samples and 237 controls. Markers tested included CD9, PSMA, PCSA, CD63, CD81, B7H3, IL 6, OPG-13, IL6R, PA2G4, EZH2, RUNX2, SERPINB3 and EpCam. Results are shown in FIG. 99, which shows mean fluorescence intensity (MFI) values for prostate cancer and normal samples for each marker tested. Higher levels of the vesicle surface markers were observed for all markers except the tetraspanins (e.g., CD63 and CD81), which serve a general vesicle biomarkers. Performance of the various markers and combinations thereof is shown in Table 28:

TABLE 28 Performance of various markers and marker combinations Marker/s Sensitivity Specificity PSMA 84% 81% PCSA 87% 82% B7-H3 81% 85% IL 6 83% 82% OPG-13 91% 81% IL6R 84% 81% PA2G4 90% 80% EZH2 81% 89% RUNX2 94% 74% SERPINB3 96% 68% OPG + PA2G4 + RUNX2 + SERPI 93% 81% PCSA + B7H3 81% 87% PA2G4 + RUNX2 + SERPI 88% 82% OPG + RUNX2 + SERPI 88% 85% OPG + PA2G4 + SERPI 86% 85% OPG + PA2G4 + RUNX2 85% 86% OPG + PA2G4 + RUNX2 + SERPI + 94% 79% PCSA + B7H3

Analysis of various markers and combinations thereof as in the Example is used for the discovery of vesicle markers for detection of disease. In addition to assay performance, selection of antibodies can be influenced by perception of the markers in the medical community, availability of reagents, ability to multiplex, and other factors.

Example 35 Vesicle Protein Array to Detect Prostate Cancer

In this example, the vesicle PCa test is performed using a protein array, more specifically an antibody array, for the detection of a set of protein biomarkers present on the vesicles from plasma of patients with prostate cancer. The array comprises capture antibodies specific to the following protein biomarkers: CD9, CD59, CD63, CD81. Vesicles are isolated as described above, e.g., in Examples 3 and 7. After filtration and isolation of the vesicles from plasma of men at risk for PCa, such as those over the age of 50, the plasma samples are incubated with an array harboring the various capture antibodies. Depending on the level of binding of fluorescently labeled detection antibodies to PSMA, PCSA, B7H3 and EpCAM that bind to the vesicles from a patient's plasma that hybridize to the array, a determination of the presence or absence of prostate cancer is made.

In a second array format, the vesicles are isolated from plasma and hybridized to an array containing CD9, CD59, CD63, CD81, PSMA, PCSA, B7H3 and EpCam. The captured vesicles are tagged with non-specific vesicle antibodies labeled with Cy3 and/or Cy5. The fluorescence is detected. Depending on the pattern of binding, a determination of the presence or absence of prostate cancer is made.

Example 36 Distinguishing BPH and PCa on Antibody Arrays

Concentrated plasma containing vesicles from 8 BPH and 8 prostate cancer stage III patients was diluted 1:30 and hybridized to a Raybiotech Human Receptor array containing 40 human cytokine receptors. The average concentration (pg/ml) of each cytokine for each group was compared using an unpaired t-test. Of the 40 receptors tested, Trappin-2, Ceacam-1, HVEM, IL-10Rb, IL-1 R4 and BCMA were the most significantly differentially expressed. See FIG. 100.

A t-test was used to determine statistical significance of the differential expression. Results are shown in Table 29:

TABLE 29 Differentiation of BPH and PCa using antibody markers Marker p-value Trappin-2 0.018 Ceacam-1 0.013 HVEM 0.0095 IL-10Rb 0.052 IL-1 R4 0.054 BCMA 0.019

By using a combination of the 6 markers to differentiate BPH and PCa, a sensitivity of 100% with 87.5% specificity for BPH was obtained.

Example 37 Distinguishing BPH and PCa Using miRs

RNA from the plasma derived vesicles of nine normal male individuals and nine individuals with stage 3 prostate cancers were analyzed on the Exiqon mIRCURY LNA microRNA PCR system panel. The Exiqon 384 well panels measure 750 miRs. Samples were normalized to control primers towards synthetic RNA spike-in from Universal cDNA synthesis kit (UniSp6 CP). Normalized values for each probe across three data sets for each indication (BPH or PCa) were averaged. Probes with an average CV % higher than 20% were not used for analysis.

Analysis of the results revealed several microRNAs that were 2 fold or more over-expressed in BPH samples compared to Stage 3 prostate cancer samples. These miRs include: hsa-miR-329, hsa-miR-30a, hsa-miR-335, hsa-miR-152, hsa-miR-151-5p, hsa-miR-200a and hsa-miR-145, as shown in Table 30:

TABLE 30 miRs overexpressed in BPH vs PCa Overexpressed in BPH v PCa Fold Change hsa-miR-329 12.32 hsa-miR-30a 6.16 hsa-miR-335 6.00 hsa-miR-152 4.73 hsa-miR-151-5p 3.16 hsa-miR-200a 3.16 hsa-miR-145 2.35

In addition, a number of miRs were overexpressed at least 2-fold in PCa versus BPH. These miRs include: hsa-miR-29a, hsa-miR-106b, hsa-miR-595, hsa-miR-142-5p, hsa-miR-99a, hsa-miR-20b, hsa-miR-373, hsa-miR-502-5p, hsa-miR-29b, hsa-miR-142-3p, hsa-miR-663, hsa-miR-423-5p, hsa-miR-15a, hsa-miR-888, hsa-miR-361-3p, hsa-miR-365, hsa-miR-10b, hsa-miR-199a-3p, hsa-miR-181a, hsa-miR-19a, hsa-miR-125b, hsa-miR-760, hsa-miR-7a, hsa-miR-671-5p, hsa-miR-7c, hsa-miR-1979, and hsa-miR-103, as shown in Table 31:

TABLE 31 miRs overexpressed in PCa vs BPH Overexpressed in PCa v BPH Fold Change hsa-miR-29a 2.18 hsa-miR-106b 2.23 hsa-miR-595 2.24 hsa-miR-142-5p 2.25 hsa-miR-99a 2.30 hsa-miR-20b 2.36 hsa-miR-373* 2.37 hsa-miR-502-5p 2.39 hsa-miR-29b 2.43 hsa-miR-142-3p 2.44 hsa-miR-663 2.51 hsa-miR-423-5p 2.55 hsa-miR-15a 2.71 hsa-miR-888 2.72 hsa-miR-361-3p 2.86 hsa-miR-365 2.90 hsa-miR-10b 2.90 hsa-miR-199a-3p 2.96 hsa-miR-181a 3.00 hsa-miR-19a 3.03 hsa-miR-125b 3.05 hsa-miR-760 3.10 hsa-miR-7a 3.77 hsa-miR-671-5p 4.11 hsa-miR-7c 5.56 hsa-miR-1979 5.80 hsa-miR-103 6.42

Example 38 miR-145 in Controls and PCa Samples

FIG. 101 illustrates a comparison of miR-145 in control and prostate cancer samples. RNA was collected as in Example 13. The controls include Caucasians >75 years old and African Americans >65 years old with PSA <4 ng/ml and a benign digital rectal exam (DRE). As seen in the figure, miR-145 was under expressed in PCa samples. miR-145 is useful for identifying those with early/indolent PCa vs those with benign prostate changes (e.g., BPH).

Example 39 Discovery of miRs Differentially Expressed in Metastatic and Non-Metastatic PCa

A panel of 720 miRs was used to compare miR expression in plasma samples from 48 patients with identified non-metastatic prostate cancer and 19 patients with identified metastatic prostate cancer. RNA derived from microvesicles of the plasma samples was evaluated on the Exiqon microRNA ready to use qRT-PCR panel version 1.0. Results were normalized to inter-plate calibrator probes and then subjected to a paired t-test. P-values were corrected with a Benjamini and Hochberg false-discovery rate test.

Table 32 shows the top four most upregulated and top four most downregulated miRs so identified. Among other target mRNAs, miR-145 is predicted to regulate BRAF, a well-characterized oncogene. miR-32 and miR-134 are predicted to regulate SMAD6, which is associated with negative regulation of BMP and TGF-beta/activin-signaling. SMAD6 expression has been associated with poor cancer prognosis.

TABLE 32 miR expression in metastatic versus non-metastatic PCa samples Significantly Up or Down Associated different miRs (p- Fold Regulated in Gene value < 0.01) Change Metastatic Disease p-value Target miR-495 117.1 Up 0.0021 BRAF miR-10a 16.4 Up 0.0050 miR-30a 11.4 Up 0.0067 miR-570 11.0 Up 0.0042 miR-32 35.0 Down 0.0042 SMAD6 miR-885-3p 9.5 Down 0.0042 miR-564 4.3 Down 0.0066 miR-134 3.7 Down 0.0086 SMAD6

The experimental setup above was repeated using Exiqon microRNA ready to use qRT-PCR panel version 2.0 and plasma samples from a cohort of 10 patients with metastatic prostate cancer and 17 patients with non-metastatic prostate cancer. Non-corrected p-values for the most significantly differently expressed miRs are shown in Table 33.

TABLE 33 miR expression in metastatic versus non-metastatic PCa samples Regulation in Fold MicroRNA p-value non metastatic Change hsa-miR-375 1.22E−04 down 36.7 hsa-miR-452 2.04E−04 down 10.3 hsa-miR-200b 0.00207 down 5.5 hsa-miR-146b-5p 0.00241 down 9.7 hsa-miR-1296 0.00430 down 4.5 hsa-miR-17* 0.0104 down 5.6 hsa-miR-100 0.0207 down 4.5 hsa-miR-574-3p 0.0222 down 2.6 hsa-miR-20a* 0.0259 down 2.3 hsa-miR-572 0.0281 up 6.5 hsa-miR-1236 0.0313 down 2.5 hsa-miR-181a 0.0374 down 9.5 hsa-miR-937 0.0446 up 5.7 hsa-miR-23a* 0.0474 down 2.0

Example 40 Detection of miRs Differentially Expressed in PCa

A panel of miRs was used to compare miR expression in plasma samples from 28 men without prostate cancer and 64 men with prostate cancer. In all cases, prostate cancer status was confirmed by biopsy. RNA derived from microvesicles of the plasma samples was evaluated on the Exiqon microRNA ready to use qRT-PCR panel. Results were normalized to inter-plate calibrator probes and then subjected to a paired t-test. P-values were corrected with a Benjamini and Hochberg false-discovery rate test. P-values for the most significantly differently expressed miRs are shown in Table 34.

TABLE 34 miR expression in metastatic versus non-metastatic PCa samples Regulation in MicroRNA p-value controls Fold Change hsa-miR-574-3p 0.0264 down 3.52 hsa-miR-331-3p 0.0264 down 6.63 hsa-miR-326 0.0264 down 5.99 hsa-miR-181a-2* 0.0264 up 2.95 hsa-miR-130b 0.0264 down 4.83 hsa-miR-301a 0.0264 down 8.18 hsa-miR-141 0.0312 down 4.13 hsa-miR-432 0.0351 down 3.83 hsa-miR-107 0.0351 down 8.29 hsa-miR-628-5p 0.0391 up 3.25 hsa-miR-625* 0.0391 down 3.98 hsa-miR-497 0.0495 down 4.35 hsa-miR-484 0.0495 down 2.90

Example 41 miRs Differentially Expressed in PCa

A panel of 84 prostate cancers and 28 control samples (biopsy controlled without prostate cancer) were analyzed using Exiqon RT-PCR panels as described herein. Using the TNM scale, the prostate cancers included 13 MX samples (did not evaluate distant metastasis), 55 M0 samples (no distant metastasis), and 16 M1 samples (confirmed distant metastasis).

miRs were detected in vesicles isolated from the patient samples. RNA was isolated from 150 μl of frozen plasma concentrate from each sample using a modified Qiagen miRneasy protocol (Qiagen GmbH, Germany). The modified protocol included treating the concentrated samples with Rnase A before isolation so that only RNA protected within vesicles was analyzed in each sample. The samples were spiked with a known quantity of C. elegans microRNA for normalization in subsequent steps. 40 ng of RNA isolated from vesicles in the sample was used for each Exiqon panel.

The Exiqon RT-PCR panel consisted of two 384 cards covering 750 miRs and control assays. The qRT-PCR assay was performed using a Sybr green assay run on an ABI 7900 (Life Technologies Corporation, Carlsbad, Calif.). Ct values for each miR assay were normalized to the Ct values of inter-plate calibrator (IPC) probes and RT-PCR controls. Several quality checks were put into place. Samples were eliminated from analysis when IPC Ct values were >25, RT-PCR Cr values were >35 and when samples did not amplify control miRs (i.e., miR-16 and miR-21). Principal component analysis of the sample data was performed using GeneSpring software (Agilent Technologies, Inc., Santa Clara, Calif.) to identify outliers. Three samples were eliminated from the analysis due for failing to qualify using these quality measures.

Data was subjected to a paired t-test between sample groups as specified below and p-values were corrected with a Benjamini and Hochberg false-discovery rate test. miRs showing the most significant p-values were validated using a Taqman probe approach.

The 84 prostate cancers and 28 controls were compared. Eighty-one (81) of 750 miR probes had a >2.0 fold change in level between the PCa and control samples (six down and 75 up). Of those 81, ten had a corrected p-value of <0.05. See Table 35.

TABLE 35 miR expression in PCa versus control samples Regulation in MicroRNA p-value PCa samples Fold Change hsa-miR-574-3p 0.0339 up 3.38 hsa-miR-141 0.0339 up 4.26 hsa-miR-331-3p 0.0442 up 5.53 hsa-miR-432 0.0442 up 3.32 hsa-miR-326 0.0339 up 5.10 hsa-miR-2110 0.0339 up 6.71 hsa-miR-107 0.0317 up 11.31 hsa-miR-130b 0.0339 up 4.66 hsa-miR-301a 0.0442 up 5.21 hsa-miR-625* 0.0442 up 3.55

A comparison as above was repeated without 16 M1 metastatic samples. This comparison identified 81 of 750 miRs with >2.0 fold change in level between the PCa and control samples. See Table 36. “Regulation in controls” refers to the upregulation (up) or downregulation (down) in control versus PCa samples.

TABLE 36 miR expression in PCa (no metastatic samples) versus control samples Regulation MicroRNA Fold change in controls hsa-miR-107 12.78 down hsa-miR-2110 6.42 down hsa-miR-326 5.75 down hsa-miR-301a 4.87 down hsa-miR-185 4.41 down hsa-miR-331-3p 4.28 down hsa-miR-373* 4.11 down hsa-miR-99a 4.06 down hsa-miR-432 3.96 down hsa-miR-625* 3.83 down hsa-miR-130b 3.60 down hsa-miR-638 3.56 down hsa-miR-425* 3.55 down hsa-miR-627 3.53 down hsa-miR-197 3.46 down hsa-miR-532-3p 3.45 down hsa-miR-124 3.42 down hsa-miR-411* 3.42 down hsa-miR-154 3.40 down hsa-miR-16-2* 3.24 down hsa-miR-574-3p 3.23 down hsa-miR-421 3.21 down hsa-miR-18a 3.17 down hsa-miR-141 3.12 down hsa-miR-423-3p 3.09 down hsa-miR-103 3.05 down hsa-miR-28-3p 3.01 down hsa-miR-375 3.00 down hsa-miR-765 2.77 down hsa-miR-362-5p 2.77 down hsa-miR-22* 2.75 down hsa-miR-181b 2.74 down hsa-miR-186 2.74 down hsa-miR-652 2.69 down hsa-miR-192 2.61 up hsa-miR-518f* 2.61 down hsa-miR-1207-5p 2.59 down hsa-miR-532-5p 2.58 down hsa-miR-484 2.56 down hsa-miR-577 2.51 up hsa-miR-379* 2.51 down hsa-miR-363 2.51 down hsa-miR-1224-3p 2.49 down hsa-miR-210 2.46 down hsa-miR-181a-2* 2.44 up hsa-miR-19b 2.41 down hsa-miR-604 2.40 down hsa-miR-125a-5p 2.39 up hsa-miR-1 2.38 down hsa-miR-518c* 2.36 down hsa-miR-95 2.33 down hsa-miR-140-5p 2.33 down hsa-miR-497 2.31 down hsa-miR-491-5p 2.28 down hsa-miR-144 2.26 down hsa-miR-18b 2.25 down hsa-miR-423-5p 2.25 down hsa-miR-665 2.25 down hsa-miR-324-3p 2.25 down hsa-miR-335 2.24 down hsa-miR-590-5p 2.20 down hsa-miR-130a 2.19 down hsa-miR-133b 2.18 down hsa-miR-1972 2.16 down hsa-miR-744* 2.15 down hsa-miR-202 2.14 up hsa-miR-30e 2.12 down hsa-miR-214 2.10 down hsa-miR-29c 2.10 down hsa-miR-20a 2.10 down hsa-miR-1247 2.09 up hsa-miR-15b* 2.08 down hsa-miR-133a 2.08 down hsa-miR-194 2.04 down hsa-miR-26b* 2.04 down hsa-miR-191 2.04 down hsa-miR-106b 2.03 down hsa-miR-485-3p 2.03 down hsa-miR-1909 2.02 down hsa-miR-628-5p 2.02 up hsa-miR-431 2.00 down

Of those 81 miRs in Table 36, nine had an uncorrected p-value <0.01. All were upregulated in PCa. See Table 37. “Regulation” refers to the upregulation (up) or downregulation (down) in PCa versus control samples.

TABLE 37 miR expression in PCa (no metastatic samples) versus control samples miR p-value Regulation Fold Change hsa-miR-107 0.0002 up 12.78 hsa-miR-326 0.0015 up 5.75 hsa-miR-432 0.0024 up 3.96 hsa-miR-574-3p 0.0029 up 3.23 hsa-miR-625* 0.0038 up 3.83 hsa-miR-2110 0.0044 up 6.42 hsa-miR-301a 0.0079 up 4.87 hsa-miR-141 0.0087 up 3.12 hsa-miR-373* 0.0090 up 4.11

In further validation of the above results, microRNA was extracted from microvesicles extracted from the plasma of 35 biopsy confirmed control men (no PCa) and 133 men with non-metastatic prostate cancer. The microRNA was evaluated for the expression of miR-107 and miR-574-3p using an ABI Taqman assay and was absolutely quantified for copy number using a synthetic standard curve. Samples were normalized for RNA isolation variation using 75 femptomol of C. elegans miR-39 as a spike-in during the RNA isolation. Results from each group were compared using a Mann-Whitney U test. Results are shown in FIG. 102A (miR-107) and FIG. 102B (miR-574-3p). The p-values were significantly different between controls and PCa samples, as indicated below each figure, thereby validating the results obtained with the Exiqon cards as shown in Tables 35-37. miR-141 was also validated with Taqman in similar experiments when comparing control and PCa samples.

A comparison as above was repeated between the 16 M1 metastatic PCa samples and 55 M0 non-metastatic PCa samples. This comparison identified 121 of 750 miRs with >2.0 fold change in level between the metastatic and non-metastatic samples. See Table 38. “Regulation in non-metastatic” refers to the upregulation (up) or downregulation (down) in non-metastatic versus metastatic PCa samples.

TABLE 38 miR expression in M1 metastatic PCa versus M0 non-metastatic PCa Regulation in Detector Fold change([M0] vs [M1]) non-metastatic hsa-miR-375 12.75 down hsa-miR-497 5.90 down hsa-miR-572 5.89 up hsa-miR-17* 5.65 down hsa-miR-197 5.45 down hsa-miR-141 5.23 down hsa-miR-148a 5.19 down hsa-miR-624* 4.93 down hsa-miR-577 4.88 down hsa-miR-32 4.82 up hsa-miR-200b 4.75 down hsa-miR-130b 4.70 down hsa-miR-210 4.60 down hsa-miR-185 4.55 up hsa-miR-451 4.51 up hsa-miR-146b-5p 4.42 down hsa-miR-452 4.17 down hsa-miR-342-3p 4.12 up hsa-miR-382 4.11 down hsa-miR-331-3p 4.10 down hsa-miR-100 3.97 down hsa-miR-181a 3.77 down hsa-miR-532-3p 3.74 down hsa-miR-636 3.62 down hsa-miR-1296 3.60 down hsa-miR-185* 3.43 up hsa-miR-1 3.41 down hsa-miR-145 3.39 down hsa-miR-10a 3.35 down hsa-miR-30b 3.30 up hsa-miR-199a-5p 3.24 down hsa-miR-30a 3.23 down hsa-miR-425 3.20 down hsa-miR-1972 3.20 down hsa-miR-20a* 3.19 down hsa-miR-142-5p 3.18 down hsa-miR-361-3p 3.17 down hsa-miR-195 3.12 down hsa-miR-1471 3.08 down hsa-miR-143 3.08 down hsa-miR-218-1* 3.08 down hsa-miR-1247 3.07 down hsa-miR-450b-3p 3.05 down hsa-miR-619 2.94 down hsa-miR-339-3p 2.90 down hsa-miR-555 2.88 down hsa-miR-629 2.87 down hsa-miR-26b 2.87 down hsa-miR-373* 2.85 up hsa-miRPlus-A1031 2.84 down hsa-miR-99b* 2.82 down hsa-miR-323-3p 2.80 down hsa-miR-486-5p 2.80 up hsa-miR-1539 2.79 up hsa-miR-17 2.79 down hsa-miR-27b 2.79 up hsa-miR-133a 2.78 down hsa-miR-144 2.76 up hsa-miR-885-5p 2.64 down hsa-let-7c* 2.64 down hsa-miR-10b 2.62 down hsa-miR-454 2.62 down hsa-miR-708* 2.59 down hsa-miR-140-5p 2.59 down hsa-miR-520h 2.58 down hsa-miR-16 2.57 up hsa-miR-886-5p 2.53 down hsa-let-7a 2.53 up hsa-miR-1913 2.50 down hsa-miR-363 2.49 down hsa-miR-517c 2.49 down hsa-miR-130a 2.46 down hsa-miR-192 2.45 down hsa-miR-132* 2.45 down hsa-miR-432 2.41 up hsa-miR-410 2.40 down hsa-miR-22 2.39 up hsa-miR-122* 2.39 down hsa-miR-937 2.39 up hsa-miR-1236 2.37 down hsa-miR-411* 2.36 up hsa-miR-128 2.35 down hsa-miR-340 2.34 down hsa-miR-152 2.31 down hsa-miR-126 2.31 up hsa-miR-30a* 2.30 down hsa-miR-186 2.30 up hsa-miR-590-5p 2.30 down hsa-miR-2110 2.30 down hsa-miR-139-5p 2.29 up hsa-miR-543 2.29 down hsa-miR-181c 2.27 down hsa-miR-582-3p 2.26 down hsa-miR-199a-3p 2.23 down hsa-miR-320b 2.22 up hsa-miR-182* 2.21 down hsa-miR-485-3p 2.19 down hsa-miR-30e 2.19 up hsa-miR-429 2.17 down hsa-miR-628-5p 2.17 down hsa-miR-142-3p 2.16 up hsa-miR-505* 2.16 down hsa-miR-628-3p 2.16 down hsa-miR-300 2.15 up hsa-miR-503 2.15 down hsa-miR-662 2.14 down hsa-miR-548c-5p 2.13 down hsa-miR-1979 2.12 up hsa-miR-433 2.11 down hsa-miR-609 2.10 down hsa-miR-18a 2.10 down hsa-miR-23a* 2.09 down hsa-miR-193a-5p 2.08 down hsa-miR-103-2* 2.08 down hsa-miR-622 2.08 up hsa-miR-320a 2.07 up hsa-miR-9 2.07 down hsa-miR-15b* 2.04 down hsa-miR-194 2.03 down hsa-miR-513a-5p 2.03 down hsa-miR-631 2.01 down

Of those 121 miRs in Table 38, nine had a p-value <0.01. See Table 39. All nine were upregulated in metastatic PCa samples.

TABLE 39 miR expression in M1 metastatic PCa versus M0 non-metastatic PCa Regulation miR p-value in metastatic Fold Change hsa-miR-200b 0.0007 up 4.75 hsa-miR-375 0.0011 up 12.75 hsa-miR-582-3p 0.0020 up 2.26 hsa-miR-17* 0.0023 up 5.65 hsa-miR-1296 0.0034 up 3.60 hsa-miR-20a* 0.0039 up 3.19 hsa-miR-100 0.0061 up 3.97 hsa-miR-452 0.0091 up 4.17 hsa-miR-577 0.0098 up 4.88

miR-17* and miR-20a* are located in the oncomir1 cluster.

Taqman assays were used to validate several miRs in the metastatic versus non-metastatic PCa setting. FIGS. 103A-D illustrate levels of miR-141 (FIG. 103A), miR-375 (FIG. 103B), miR-200b (FIG. 103C) and miR-574-3p (FIG. 103D) in vesicles isolated from metastatic (M1) and non-metastatic (M0) prostate cancer samples. In all cases, the p-values were significant when comparing miR levels between the metastatic and non-metastatic samples.

A summary of the Taqman validation results is shown in Table 40. In the table, M0 and M1 refer to sample numbers used to test each microRNA. P-values were significant in all cases.

TABLE 40 miR expression in M1 metastatic PCa versus M0 non-metastatic PCa by Taqman miR M0 M1 p-value hsa-miR-200b 73 33 0.05 hsa-miR-375 71 33 0.0001 hsa-miR-141 73 39 0.0001 hsa-miR-331-3p 64 27 0.002 hsa-miR-181a 65 27 0.002 hsa-miR-574-3p 65 32 0.0001

Levels of hsa-miR-141 and hsa-miR-375 from the RNA of serum-derived vesicles in a separate cohort of 47 metastatic prostate cancer patients was also found to be significantly higher than in 72 non-recurring prostate cancer patients (p=0.0001).

Vesicles from plasma and serum are reliable sources of microRNA for biomarkers. Two miRs, hsa-miR-107 and 574-3p were found particularly elevated in prostate cancer samples compared to biopsy confirmed controls. In metastatic plasma-derived vesicle samples, several miRs were found to be significantly elevated, and 2 of these, hsa-miR-141 and hsa-miR-375, were also found to be particularly elevated in metastatic serum-derived vesicles.

Example 42 miRs to Enhance Vesicle Diagnostic Assay Performance

As described herein, vesicles are concentrated in plasma patient samples and assessed to provide a diagnostic, prognostic or theranostic readout. Vesicle analysis of patient samples includes the detection of vesicle surface biomarkers, e.g., surface antigens, and/or vesicle payload, e.g., mRNAs and microRNAs, as described herein. The payload within the vesicles can be assessed to enhance assay performance. For example, FIG. 104A illustrates a scheme for using miR analysis within vesicles to convert false negatives into true positives, thereby improving sensitivity. In this scheme, samples called negative by the vesicle surface antigen analysis are further confirmed as true negatives or true positives by assessing payload with the vesicles Similarly, FIG. 104B illustrates a scheme for using miR analysis within vesicles to convert false positives into true negatives, thereby improving specificity. In this scheme, samples called positive by the vesicle surface antigen analysis are further confirmed as true negatives or true positives by assessing payload with the vesicles.

A diagnostic test for prostate cancer includes isolating vesicles from a blood sample from a patient to detect vesicles indicative of the presence or absence of prostate cancer. See, e.g., Examples 27-34. The blood can be serum or plasma. The vesicles are isolated by capture with “capture antibodies” that recognize specific vesicle surface antigens. The surface antigens for the prostate cancer diagnostic assay include the tetraspanins CD9, CD63 and CD81, which are generally present on vesicles in the blood and therefore act as general vesicle biomarkers, the prostate specific biomarkers PSMA and PCSA, and the cancer specific biomarker B7H3. The capture antibodies are tethered to fluorescently labeled beads, wherein the beads are differentially labeled for each capture antibody. Captured vesicles are further highlighted using fluorescently labeled “detection antibodies” to the tetraspanins CD9, CD63 and CD81. Fluorescence from the beads and the detection antibodies is used to determine an amount of vesicles in the plasma sample expressing the surface antigens for the prostate cancer diagnostic assay. The fluorescence levels in a sample are compared to a reference level that can distinguish samples having prostate cancer. In this Example, microRNA analysis is used to enhance the performance of the vesicle-based prostate cancer diagnostic assay.

FIG. 104C shows the results of detection of miR-107 in samples assessed by the vesicle-based prostate cancer diagnostic assay. FIG. 104D shows the results of detection of miR-141 in samples assessed by the vesicle-based prostate cancer diagnostic assay. In the figure, normalized levels of the indicated miRs are shown on the Y axis for true positives (TP) called by the vesicle diagnostic assay, true negatives (TN) called by the vesicle diagnostic assay, false positives (FP) called by the vesicle diagnostic assay, and false negatives (FN) called by the vesicle diagnostic assay. As shown in FIG. 104C, the use of miR-107 enhances the sensitivity of the vesicle assay by distinguishing false negatives from true negative (p=0.0008) Similarly, FIG. 104D also shows that the use of miR-141 enhances the sensitivity of the vesicle assay by distinguishing false negatives from true negative (p=0.0001). Results of adding miR-141 are shown in Table 41. miR-574-3p performs similarly.

TABLE 41 Addition of miR-141 to vesicle-based test for PCa Without miR-141 With miR-141 Sensitivity 85% 98% Specificity 86% 86%

In this Example, vesicles are detected via surface antigens that are indicative of prostate cancer, and the performance of the signature is further bolstered by examining miRs within the vesicles, i.e., sensitivity is increased without negatively affecting specificity. This general methodology can be extended for any setting in which vesicles are profiled for surface antigens or other informative characteristic, then one or more additional biomarker is used to enhance characterization. Here, the one or more additional biomarkers are miRs. They could also comprise mRNA, soluble protein, lipids, carbohydrates and any other vesicle-associated biological entities that are useful for characterizing the phenotype of interest.

Example 43 Comparison of miR Expression Patterns in Plasma, Serum and Cell Line Vesicles

Agilent v3 miRNA microarrays (Agilent Technologies, Inc., Santa Clara, Calif.) were used to compare expression of vesicle-derived microRNA (miR) between plasma and serum from patients with prostate cancer, healthy controls, one prostate cancer cell line, and prostate tumor and normal tissue. Total RNA was isolated from plasma and cell line vesicles using the Qiagen miReasy kit and from serum using the Exomir extraction method (Bioo Scientific Corp., Austin, Tex.). 100 ng of each sample was hybridized to the microarrays and the extracted data was analyzed with the GeneSpring software package. Hierarchal clustering on both samples and genes demonstrated a distinct expression pattern of plasma vesicles compared to cell-line derived vesicles and tumor tissue. Serum and plasma from prostate cancer patients and normal controls were evaluated for significantly differentially expressed microRNAs. Vesicles derived from peripheral blood offer a unique source for blood-based miR analysis.

Example 44 Isolating Subpopulations of Vesicles and Subsequent miR Profiles

In this Example, microRNA (miR) expression patterns were examined in circulating microvesicle subpopulations that were defined based on surface protein composition. Vesicles isolated from a prostate cancer cell line (VCaP) were flow sorted based on their surface protein composition using methodology as described herein. The vesicles were evaluated for differential expression of miRs. Phycoerythrin-labeled antibodies targeting EpCam, CD63, or B7-H3 were used to sort the subpopulations of vesicles by fluorescence-activated cell sorting. Vesicles were sorted on a Beckman-Coulter MoFlo XDP (Beckman Coulter, Inc., Brea, Calif.) so that each vesicle could be analyzed as an individual particle. There was a significant shift in the intensity of the FL2 channel over the isotype control due to the abundance of the antigen on the surface of the vesicles. The sorted subpopulations of vesicles were subsequently profiled by miR expression. The miR profiles for the EpCam, CD63, and B7-H3 positive subpopulations were compared to the profile of the total VCaP vesicle population. Differential miR expression patterns were observed across the subpopulations and all expression patterns were distinct from that observed in the total population. Patterns of both over- and under-expression of miRs were observed between groups. These data show that subpopulations of vesicles can be distinguished and separated based on surface protein markers as well as their genetic content, in this case miRs. The ability to isolate tissue-specific vesicle populations from patient plasma based on surface protein composition and then analyze them based on both surface protein composition and genetic content can be used for diagnostic, prognostic, and theranostic applications as described herein.

Example 45 MicroRNA Biomarkers in Men with Prostate Cancer and Low PSA

Although an enlarged prostate combined with low PSA, e.g., less than 4 ng/ml, may indicate benign prostate hyperplasia (BPH), these clinical observations are indicative of prostate cancer rather than BPH in some cases. A biomarker that can distinguish between these two groups would allow for the early detection of prostate cancer in symptomatic men with low PSA.

Using methodology described herein, vesicles were isolated from plasma samples from 13 control patients with PSA <4.0 ng/ml (group 1), 15 control patients with PSA ≧4.0 ng/ml (group 2), nine non-metastatic prostate cancer patients with PSA <4.0 ng/ml (group 3) and 59 non-metastatic prostate cancer patients with PSA ≧4.0 ng/ml (group 4). MicroRNA payload was isolated from the vesicles and was examined using the Exiqon RT-PCR panel consisting of 750 miR probes as described herein. Normalized results were compared between prostate cancer patients with PSA >4.0 (group 4) and prostate cancer patients with PSA <4.0 (group 3). 344 miR probes were found to have a fold change greater than two-fold between these groups. See Table 42. In Table 42, “Fold change” refers to the change in levels between groups 3 and 4 and “Regulation” refers to upregulation (up) or downregulation (down) in group 4 as compared to group 3.

TABLE 42 Fold change in miR levels in PCa samples with PSA < or ≧4.0 ng/ml MicroRNA Fold change Regulation hsa-miR-143 41.44 down hsa-miR-432 31.23 down hsa-miR-425 18.66 down hsa-miR-32 17.55 down hsa-miR-424 15.78 down hsa-miR-96 14.72 down hsa-miR-629 14.54 down hsa-miR-532-5p 13.43 down hsa-miR-215 13.40 down hsa-miR-920 12.21 down hsa-miR-421 11.78 down hsa-miR-204 11.18 down hsa-miR-29a 11.04 down hsa-miR-148a 10.62 down hsa-miR-19b 10.00 down hsa-miR-595 9.96 down hsa-miR-590-5p 9.93 down hsa-miR-518f 9.76 down hsa-miR-518f* 9.68 down hsa-miR-766 9.49 down hsa-miR-22* 9.01 down hsa-miR-491-5p 8.95 down hsa-miR-29b 8.83 down hsa-miR-144 8.52 down hsa-miR-451 8.33 down hsa-miR-376a 8.27 down hsa-miR-577 8.22 down hsa-miR-151-3p 8.18 down hsa-let-7f 7.74 down hsa-miR-188-3p 7.52 up hsa-let-7i 7.52 down hsa-miR-19a 7.45 down hsa-miR-616* 7.35 down hsa-miR-140-3p 7.28 down hsa-miR-18a* 7.24 down hsa-miR-154* 7.11 down hsa-miR-423-5p 7.11 down hsa-miR-192 7.01 down hsa-miR-212 7.00 down hsa-miR-107 6.95 down hsa-miR-16-2* 6.89 down hsa-miR-205 6.84 down hsa-miR-199a-3p 6.84 down hsa-miR-101 6.78 down hsa-miR-130a 6.75 down hsa-miR-15a 6.64 down hsa-miR-363 6.58 down hsa-miR-30b* 6.56 down hsa-miR-146b-5p 6.54 down hsa-miR-142-5p 6.48 down hsa-miR-197 6.44 down hsa-miR-339-5p 6.41 down hsa-miR-140-5p 6.39 down hsa-miR-450a 6.27 down hsa-miR-624* 6.21 down hsa-miR-122 6.15 down hsa-miR-665 6.13 down hsa-miR-125b 6.06 down hsa-miR-937 6.05 down hsa-miR-148b 5.99 down hsa-miR-106b* 5.99 down hsa-miR-769-5p 5.95 down hsa-miR-1255b 5.94 down hsa-miR-517* 5.87 down hsa-miR-517a 5.85 down hsa-let-7d 5.68 down hsa-miR-365* 5.67 down hsa-miR-302d* 5.57 down hsa-miR-221* 5.54 down hsa-miR-103-2* 5.47 down hsa-miR-136 5.43 down hsa-let-7g 5.43 down hsa-miR-424* 5.25 down hsa-miR-124 5.12 down hsa-miR-103 5.10 down hsa-miR-23b* 5.09 down hsa-miR-191 5.08 down hsa-miR-221 5.06 down hsa-miR-324-5p 5.06 down hsa-miR-330-5p 5.03 down hsa-miR-302b 5.01 down hsa-miR-570 4.96 down hsa-miR-105* 4.95 down hsa-miR-15b 4.95 down hsa-miR-29c 4.91 down hsa-miR-497 4.76 down hsa-miR-617 4.74 down hsa-miR-1200 4.69 down hsa-miR-29a* 4.62 down hsa-miR-1468 4.62 down hsa-miR-24 4.58 down hsa-miR-181a* 4.56 down hsa-miR-211 4.56 down hsa-let-7b 4.55 down hsa-miR-103-as 4.50 down hsa-miR-302a 4.45 down hsa-miR-30b 4.45 down hsa-miR-765 4.44 down hsa-miRPlus-A1031 4.43 down hsa-miR-181a 4.42 down hsa-miR-25 4.38 down hsa-miR-324-3p 4.37 down hsa-miR-199a-5p 4.32 down hsa-miR-375 4.32 down hsa-miR-887 4.30 down hsa-miR-1179 4.22 down hsa-miR-27a 4.20 down hsa-miR-411* 4.19 down hsa-miR-10a 4.19 down hsa-miR-609 4.18 down hsa-miR-342-3p 4.18 down hsa-miR-219-2-3p 4.16 down hsa-miR-299-5p 4.14 down hsa-miR-1 4.14 down hsa-miR-23a* 4.05 down hsa-miR-31 4.04 down hsa-miR-1260 4.01 down hsa-miR-335 3.99 down hsa-miR-93 3.98 down hsa-miR-148b* 3.93 down hsa-miR-376b 3.91 down hsa-miR-376c 3.90 down hsa-miR-16 3.89 down hsa-miR-30c 3.88 down hsa-miR-21* 3.87 down hsa-miR-185* 3.87 down hsa-miR-139-5p 3.83 down hsa-miR-331-3p 3.82 down hsa-miR-210 3.80 down hsa-miR-371-3p 3.80 down hsa-miR-328 3.79 down hsa-miR-886-5p 3.77 up hsa-let-7c* 3.77 down hsa-miR-484 3.74 down hsa-miR-198 3.72 down hsa-miR-584 3.72 down hsa-miR-99b* 3.71 down hsa-miR-619 3.69 down hsa-miR-654-3p 3.66 down hsa-miR-377 3.65 down hsa-miR-636 3.64 down hsa-miR-921 3.63 down hsa-miR-518e* 3.59 down SNORD49A 3.56 down hsa-miR-188-5p 3.54 down hsa-miR-532-3p 3.52 down hsa-miR-1266 3.50 down hsa-miR-410 3.50 down hsa-miR-34b* 3.50 up hsa-miR-505* 3.49 down hsa-miR-18a 3.49 down hsa-miR-297 3.43 down hsa-miR-940 3.43 down hsa-miR-582-3p 3.43 down hsa-miR-7-2* 3.43 down hsa-miR-30e 3.42 down hsa-miRPlus-A1027 3.42 down hsa-miR-146a 3.39 down hsa-miR-21 3.38 down hsa-miR-431 3.38 down hsa-miR-495 3.38 down hsa-miR-106a 3.37 down hsa-miR-574-3p 3.37 down hsa-miR-526b 3.37 down hsa-miR-651 3.37 down hsa-miR-92a 3.37 down hsa-miR-182 3.36 down hsa-miR-631 3.34 down hsa-miR-675* 3.34 down hsa-miR-374b* 3.34 down hsa-miR-300 3.31 down hsa-miRPlus-C1070 3.31 down hsa-miR-135a 3.31 down hsa-miR-449a 3.27 down hsa-miR-187 3.23 down hsa-miR-19b-1* 3.21 down hsa-miR-412 3.19 down hsa-miR-345 3.18 down hsa-miR-202 3.14 down hsa-miR-524-5p 3.14 down hsa-miR-10b 3.14 down hsa-miR-452 3.14 down hsa-miR-141 3.13 down hsa-miR-217 3.11 down hsa-miR-17* 3.10 down hsa-miR-200a 3.10 down hsa-miR-523 3.08 down hsa-miR-642 3.07 down hsa-miR-378 3.05 down hsa-miR-99b 3.04 down hsa-miR-339-3p 3.02 down hsa-miR-942 3.01 down hsa-miR-555 2.99 down hsa-miR-222 2.99 down hsa-miR-151-5p 2.98 down hsa-miR-634 2.97 down hsa-miR-628-5p 2.96 down hsa-miR-223 2.95 down hsa-miR-486-5p 2.95 down hsa-miR-142-3p 2.93 down hsa-miR-130b 2.92 down hsa-miR-220b 2.92 down hsa-miR-218 2.90 down hsa-miR-132 2.89 down hsa-miR-20a 2.89 down hsa-miR-320a 2.88 down hsa-miR-553 2.87 down hsa-miR-27b 2.87 down hsa-miR-620 2.86 down hsa-miR-28-5p 2.84 down hsa-miR-1913 2.83 down hsa-miR-150 2.83 down hsa-miR-301b 2.83 down hsa-miR-520d-3p 2.82 up hsa-miR-126 2.82 down hsa-miR-654-5p 2.81 down hsa-miR-558 2.81 down hsa-miR-586 2.80 down hsa-miR-516b 2.77 down hsa-miR-1269 2.77 down hsa-miR-658 2.76 down hsa-miR-92a-1* 2.75 down hsa-miR-92a-2* 2.75 down hsa-miR-625* 2.74 down hsa-miR-1205 2.74 down hsa-miR-224* 2.74 down hsa-miR-326 2.72 down hsa-miR-573 2.72 down hsa-miR-1909 2.72 down hsa-miR-500 2.71 down hsa-miR-7 2.71 down hsa-miR-583 2.70 down hsa-miR-185 2.69 down hsa-miR-943 2.69 down hsa-miR-544 2.69 down hsa-miR-9 2.68 down hsa-miR-22 2.67 down hsa-miR-1252 2.67 down hsa-miR-876-3p 2.64 down hsa-miR-890 2.64 down hsa-miR-520c-3p 2.63 down hsa-miR-1270 2.63 down hsa-miR-296-3p 2.62 down hsa-miR-450b-3p 2.62 down hsa-miR-200b 2.62 down hsa-miR-576-5p 2.61 down hsa-miR-767-5p 2.61 down hsa-miR-888 2.61 down hsa-miR-216a 2.60 down hsa-miRPlus-C1089 2.60 down hsa-miR-340* 2.60 down hsa-miR-214* 2.59 down hsa-miR-550 2.59 down hsa-miR-510 2.58 down hsa-miR-34c-3p 2.57 down hsa-miR-135b 2.57 down hsa-miR-106b 2.56 down hsa-miR-512-3p 2.56 down hsa-miR-1237 2.56 down hsa-miR-543 2.56 up hsa-miR-18b 2.54 down hsa-miR-125a-5p 2.53 down hsa-miR-135b* 2.53 down hsa-miR-760 2.52 up hsa-miR-184 2.51 down hsa-miR-629* 2.47 down hsa-miR-1238 2.47 down hsa-miR-138 2.47 down hsa-miR-365 2.46 down hsa-let-7g* 2.45 down hsa-miR-744 2.45 down hsa-miR-133a 2.45 down hsa-miR-557 2.44 down hsa-miR-454* 2.44 down hsa-miR-26b* 2.44 down hsa-miR-593* 2.43 down hsa-miR-548c-5p 2.42 down hsa-miR-653 2.41 down hsa-miR-708 2.41 down hsa-miR-15a* 2.41 down hsa-miR-452* 2.40 down hsa-miR-186 2.40 down hsa-miR-1972 2.39 down hsa-miR-101* 2.39 down hsa-miR-148a* 2.38 down hsa-miR-548a-5p 2.37 down hsa-miR-98 2.36 down hsa-miR-33a* 2.36 down hsa-miR-877* 2.36 down hsa-miRPlus-D1061 2.35 down hsa-miR-17 2.35 down hsa-miR-608 2.34 down hsa-miR-92b* 2.34 down hsa-miR-154 2.33 down hsa-miR-27b* 2.33 down hsa-miR-93* 2.33 down hsa-miR-203 2.32 down hsa-miR-603 2.30 up hsa-miR-30d* 2.29 down hsa-miR-373 2.28 down hsa-let-7f-1* 2.28 down hsa-miR-541* 2.27 down hsa-miR-187* 2.27 down hsa-miR-1265 2.26 down hsa-miR-23a 2.25 down hsa-miR-30c-1* 2.25 down hsa-miR-362-5p 2.25 down hsa-miR-30a* 2.25 down hsa-miR-200b* 2.25 down hsa-miR-744* 2.24 down hsa-miR-1979 2.23 down hsa-let-7b* 2.23 down hsa-miR-132* 2.23 down hsa-miR-571 2.22 down hsa-miR-425* 2.20 down hsa-miR-194* 2.20 down hsa-miR-145* 2.17 down hsa-miR-551b 2.17 down hsa-miR-720 2.16 down hsa-miR-302d 2.16 down hsa-miR-195 2.16 down hsa-miR-194 2.16 down hsa-miR-885-3p 2.16 down hsa-miR-579 2.15 down hsa-miR-361-3p 2.15 down hsa-miR-542-5p 2.15 down hsa-miR-320b 2.13 down hsa-miR-155 2.13 up hsa-miR-548j 2.10 down hsa-miR-616 2.10 down hsa-miR-502-5p 2.10 down hsa-miR-662 2.09 down hsa-miR-137 2.08 down hsa-miR-218-1* 2.08 down hsa-miR-1537 2.07 down hsa-miR-143* 2.07 down hsa-miR-1227 2.06 down hsa-miR-23b 2.05 down hsa-miR-675b 2.03 down hsa-miR-323-3p 2.03 down hsa-miR-889 2.02 down hsa-miR-485-3p 2.02 up hsa-miR-545 2.01 up hsa-miR-340 2.00 down

An unpaired t-test with a Benjamini and Hochberg false discovery rate (FDR) of <0.05 was performed on the microRNAs in Table 42. See Benjamini and Hochberg. “Controlling the false discovery rate: a practical and powerful approach to multiple testing” Journal of the Royal Statistical Society, Series B (Methodological) 57: 289-300 (1995). 32 significant probes were identified that met these criteria. See Table 43. In Table 43, corrected p-values are shown and regulation refers to the upregulation (up) or downregulation (down) in group 3 as compared to group 4.

TABLE 43 miR levels in miR levels in PCa samples with PSA < or ≧ 4.0 ng/ml MicroRNA p-value Regulation Fold Change hsa-miR-432 0.0025 up 31.23 hsa-miR-23b* 0.0073 up 5.09 hsa-miR-518f 0.0073 up 9.76 hsa-miR-96 0.0073 up 14.72 hsa-miR-154* 0.0084 up 7.11 hsa-miR-143 0.0157 up 41.44 hsa-miR-424* 0.0157 up 5.25 hsa-miR-219-2-3p 0.0257 up 4.16 hsa-miR-517a 0.0313 up 5.85 hsa-let-7b 0.0313 up 4.55 hsa-miR-450a 0.0344 up 6.27 hsa-miR-204 0.0415 up 11.18 hsa-miR-19b-1* 0.0415 up 3.21 hsa-miR-217 0.0441 up 3.11 hsa-miR-181a* 0.0441 up 4.56 hsa-miR-150 0.0441 up 2.83 hsa-miR-629 0.0442 up 14.54 hsa-miR-148b* 0.0442 up 3.93 hsa-miR-617 0.0442 up 4.74 hsa-miR-18a* 0.0442 up 7.24 hsa-miR-517* 0.0442 up 5.87 hsa-miR-451 0.0442 up 8.33 hsa-miR-595 0.0442 up 9.96 hsa-miR-634 0.0442 up 2.97 hsa-miR-93 0.0442 up 3.98 hsa-miR-1270 0.0442 up 2.63 hsa-miR-424 0.0442 up 15.78 hsa-miR-299-5p 0.0442 up 4.14 hsa-miR-365* 0.0442 up 5.67 hsa-miR-215 0.0442 up 13.40 hsa-miR-769-5p 0.0442 up 5.95 hsa-miR-1205 0.0442 up 2.74

The set of 32 probes in Table 43 was examined in the context of the four aforementioned groups 1-4. Six microRNAs were found with expression differences greater than 5-fold and wherein the mean of the prostate cancer PSA <4.0 group did not overlap with the interquartile range of the control groups. These miRs can distinguish cancer from no cancer in symptomatic men with PSA <4.0. This selection consisted of the miRs shown in FIGS. 105A-105F: hsa-miR-432 (FIG. 105A), hsa-miR-143 (FIG. 105B), hsa-miR-424 (FIG. 105C), hsa-miR-204 (FIG. 105D), hsa-miR-581f (FIG. 105E) and hsa-miR-451 (FIG. 105F). In the figures, the X axis shows the four groups of samples: “Control no” are control patients with PSA ≧4.0 ng/ml (group 2); “Control yes” are control patients with PSA <4.0 (group 1) ng/ml; “Diseased no” are prostate cancer patients with PSA ≧4.0 (group 4) ng/ml; and “Diseased yes” are prostate cancer patients with PSA <4.0 (group 3) ng/ml.

Example 46 Prostate Cancer-Related microRNAs

FIG. 106 illustrates the levels of microRNAs miR-29a and miR-145 in vesicles isolated from plasma samples from prostate cancer (PCa) and controls. For miR-29a, data is shown for 81 controls and 130 PCa cases. For miR-145, data is shown for 81 controls and 126 PCa cases. A paired t-test revealed that the levels of miR-29a (p<0.001) and miR-145 (p<0.0001) were significantly different between cases and controls.

Example 47 microRNAs Before and after Treatment for PCa

Fifteen prostate cancer patients had a plasma sample drawn before and after treatment. The treatment was either radical prostatectomy or radiation therapy. RNA derived from microvesicles of the plasma samples was evaluated on the Exiqon microRNA ready to use qRT-PCR panel. See Examples 17-18 for further details. Results were normalized to inter-plate calibrator probes and then subjected to a paired t-test. P-values were corrected with a Benjamini and Hochberg false-discovery rate test. Table 44 shows several statistically significant miRs from this comparison of miR expression before and after treatment. Fold-change is the amount increase of these miRs in samples before treatment compared to samples after treatment. All miRs in Table 44 were over-expressed in the before-treatment samples.

TABLE 44 Differentially expressed miRs before and after treatment for PCa Corrected p- miR Fold Change value hsa-miR-1974 12.08 0.0025 hsa-miR-27b 7.8 0.0025 hsa-miR-103 10.43 0.0067 hsa-miR-146a 9.24 0.0067 hsa-miR-22 4.06 0.0067 hsa-miR-382 8.12 0.0105 hsa-miR-23a 3.93 0.0181 hsa-miR-376c 3.51 0.0181 hsa-miR-335 8.26 0.0181 hsa-miR-142-5p 3.85 0.0202 hsa-miR-221 7.08 0.0245 hsa-miR-142-3p 3.8 0.0302 hsa-miR-151-3p 9.1 0.0398 hsa-miR-21 3.81 0.0398

Example 48 Vesicle Isolation and Detection Methods

A number of technologies known to those of skill in the art can be used for isolation and detection of vesicles to carry out the methods of the invention in addition to those described above. The following is an illustrative description of several such methods.

Glass Microbeads.

Available as VeraCode/BeadXpress from Illumina, Inc. San Diego, Calif., USA. The steps are as follows:

-   -   1. Prepare the beads by direct conjugation of antibodies to         available carboxyl groups.     -   2. Block non specific binding sites on the surface of the beads.     -   3. Add the beads to the vesicle concentrate sample.     -   4. Wash the samples so that unbound vesicles are removed.     -   5. Apply fluorescently labeled antibodies as detection         antibodies which will bind specifically to the vesicles.     -   6. Wash the plate, so that the unbound detection antibodies are         removed.     -   7. Measure the fluorescence of the plate wells to determine the         presence the vesicles.

Enzyme Linked Immunosorbent Assay (ELISA).

Methods of performing ELISA are well known to those of skill in the art. The steps are generally as follows:

-   -   1. Prepare a surface to which a known quantity of capture         antibody is bound.     -   2. Block non specific binding sites on the surface.     -   3. Apply the vesicle sample to the plate.     -   4. Wash the plate, so that unbound vesicles are removed.     -   5. Apply enzyme linked primary antibodies as detection         antibodies which also bind specifically to the vesicles.     -   6. Wash the plate, so that the unbound antibody-enzyme         conjugates are removed.     -   7. Apply a chemical which is converted by the enzyme into a         color, fluorescent or electrochemical signal.     -   8. Measure the absorbency, fluorescence or electrochemical         signal (e.g., current) of the plate wells to determine the         presence and quantity of vesicles.

Electrochemiluminescence Detection Arrays.

Available from Meso Scale Discovery, Gaithersburg, Md., USA:

-   -   1. Prepare plate coating buffer by combining 5 mL buffer of         choice (e.g. PBS, TBS, HEPES) and 75 μL of 1% Triton X-100         (0.015% final).     -   2. Dilute capture antibody to be coated.     -   3. Prepare 5 μL of diluted a capture antibody per well using         plate coating buffer (with Triton).     -   4. Apply 5 μL of diluted capture antibody directly to the center         of the working electrode surface being careful not to breach the         dielectric. The droplet should spread over time to the edge of         the dielectric barrier but not cross it.     -   5. Allow plates to sit uncovered and undisturbed overnight.

The vesicle containing sample and a solution containing the labeled detection antibody are added to the plate wells. The detection antibody is an anti-target antibody labeled with an electrochemiluminescent compound, MSD SULFO-TAG label. Vesicles present in the sample bind the capture antibody immobilized on the electrode and the labeled detection antibody binds the target on the vesicle, completing the sandwich. MSD read buffer is added to provide the necessary environment for electrochemiluminescence detection. The plate is inserted into a reader wherein a voltage is applied to the plate electrodes, which causes the label bound to the electrode surface to emit light. The reader detects the intensity of the emitted light to provide a quantitative measure of the amount of vesicles in the sample.

Nanoparticles.

Multiple sets of gold nanoparticles are prepared with a separate antibody bound to each. The concentrated microvesicles are incubated with a single bead type for 4 hours at 37° C. on a glass slide. If sufficient quantities of the target are present, there is a colorimetric shift from red to purple. The assay is performed separately for each target. Gold nanoparticles are available from Nanosphere, Inc. of Northbrook, Ill., USA.

Nanosight.

A diameter of one or more vesicles can be determined using optical particle detection. See U.S. Pat. No. 7,751,053, entitled “Optical Detection and Analysis of Particles” and issued Jul. 6, 2010; and U.S. Pat. No. 7,399,600, entitled “Optical Detection and Analysis of Particles” and issued Jul. 15, 2010. The particles can also be labeled and counted so that an amount of distinct vesicles or vesicle populations can be assessed in a sample.

Example 49 Protocol for Vesicle Concentration from Plasma

In this example, vesicles are concentrated in plasma patient samples. The protocols can be used for vesicle analysis from patient samples, including the detection of vesicle surface biomarkers, e.g., surface antigens, and/or vesicle payload, e.g., mRNAs and microRNAs, as described herein.

Equipment, Reagents & Supplies:

Equipment

-   -   a. Thermo Scientific Sorvall Legend RT Plus Series Benchtop         Centrifuge with 15 ml swinging bucket rotor. Part Number:         75004377 (Thermo Scientific, Part of Thermo Fisher Scientific,         Waltham, Mass.)     -   b. Class II Biosafety Cabinet for plasma handling     -   c. Pipettors: 20 μl, 200 μl, 1000 μl     -   d. Serological pipettor, Pipette Boy, VWR, catalog number:         14222-180 (VWR International, LLC, West Chester, Pa.)     -   e. VWR digital vortex mixer, catalog number: 14005-824     -   f. Computer with interne access

Reagents

-   -   a. 1×PBS, Sigma pH 7.4. catalog number: P-3813 (Sigma, Saint         Louis, Mo., part of Sigma-Aldrich, Inc.)     -   b. Molecular Biology Reagent Water, Sigma, catalog number: W4502

Supplies

-   -   a. 0.8 μm Millex-AA syringe-driven filter unit, Millipore. Part         number: SLAA033SB (Millipore, Billerica, Mass.)     -   b. Pierce concentrators, 150K MWCO (molecular weight cut off)         7 ml. Part number: 89922 (Pierce, Part of Thermo Fisher         Scientific Inc. Rockford, Ill.)     -   c. Non Sterile BD Luer Lock Syringe, 10 ml. Part number: 301029         (BD, Franklin Lakes, N.J.)     -   d. USA Scientific co-polymer 1.5 ml non-binding tubes, USA         Scientific, catalog number 1415-2500 (USA Scientific, Inc.,         Ocala, Fla.)     -   e. 5 ml sterile plugged serological pipettes, Fisher, catalog         number 13-678-11D (Fisher Scientific, Part of Thermo Fisher         Scientific, Pittsburgh, Pa.)     -   f. Ice bucket, Fisher, catalog number 02-591-46     -   g. Tube racks, Fisher, catalog number 05-541-38     -   h. 4-way racks, Fisher, catalog number 03-448-17     -   i. 50 ml conical, VWR, catalog number 21008-951     -   j. Floating tube racks, VWR, catalog number 60986-100     -   k. 1 liter beakers, VWR, catalog number 89000-226     -   l. 10/20 μl filtered pipet tips, Rainin, catalog number GP-L10F         (Rainin Instrument, LLC, Oakland, Calif., a METTLER TOLEDO         Company)     -   m. 200 μl filtered pipet tips, Rainin, catalog number GP-L200F     -   n. 1000 μl filtered pipet tips, Rainin, catalog number GP-L1000F     -   o. Personal protective equipment

Quality Control:

-   -   a. Samples with less than 900 μl volume may provide suboptimal         results and should be avoided.     -   b. Samples that have been through more than one freeze thaw         cycle may provide suboptimal results and should be avoided.     -   c. The 150K MWCO columns can be damaged by pipette tips or         during the manufacture process. The determination of a column         compromise can be assessed by examining the filtrate. If the         filtrate appears to contain a heavy amount of plasma and the         column itself contains a low volume of plasma (<100 μl) then it         is likely the 7 ml 150K MWCO column has been compromised. If a         column has been suspected of being compromised then the sample         will need to be re-concentrated with another plasma aliquot from         the same patient.

Procedure:

Selecting Samples for Concentration

-   -   a. Enter sample information for the selected samples from the         sample database into a Microsoft Excel spreadsheet (Microsoft         Corp, Redmond, Wash.).     -   b. Print a copy of the Plasma Concentration Bench Sheet from         Excel.

Filter Procedure for Plasma Samples

-   -   a. Fill ice bucket with water from cold tap faucet.     -   b. Find plasma samples listed on the Plasma Concentration Bench         Sheet and remove samples from −80° C. (−65° C. to −85° C.)         freezer. Any remaining cryovials will continue being stored in         the same box at −80° C. (−65° C. to −85° C.) in the event that         an additional aliquot of plasma is required for testing.     -   c. Thaw samples in water drawn in a) by placing inside floating         tube rack. Check plasma after 10 minutes and if all plasma         samples are not completely thawed, leave plasma in water and         check at 5 minute intervals until all plasma samples are thawed.     -   d. During thawing step, remove labels from blue folder and affix         one side label to each 7 ml 150K MWCO columns (1 per plasma         sample) and place in 4-way tube rack. Record lot numbers for         columns on Plasma Concentration Bench Sheet.     -   e. Pour Molecular Biology Reagent water into 1 liter beaker.     -   f. For each sample to be run, fill a 10 ml syringe with 4 mls of         Molecular Biology Reagent water by submerging syringe tip into         water in beaker and drawing up the plunger.     -   g. Attach a 0.8 μm Millipore filter to each syringe tip and pass         contents through the filter onto a 7 ml 150K MWCO column.     -   h. Cap the columns, place in the swing bucket centrifuge and         centrifuge at 1000×g in Sorvall Legend XTR Benchtop centrifuge         for 4 minutes at 20° C. (16° C. to 24° C.).     -   i. While spinning columns, remove Millipore filter from syringe,         pull plunger out of syringe, and replace filter at end of         syringe.     -   j. When centrifuge is done spinning, discard flow through from         the 7 ml 150K MWCO column as well as any residual water left in         the upper filter.     -   k. Place syringe and filter on open 7 ml 150K MWCO column. Fill         open end of syringe with 5.2 ml of 1×PBS prepared in sterile         molecular grade water.     -   l. Using a p1000 pipette, assess and record volume of patient         plasma on Plasma Concentration Bench Sheet.     -   m. If sample is less than 900 μl, a new test on another plasma         aliquot should be performed for that patient. Aquire another         sample for the patient and update the Plasma Concentration Bench         Sheet accordingly.     -   n. Pipette patient plasma (900-1000 μl) into the PBS in the         syringe, pipette mix twice, and discard plasma tube, along with         any remaining patient plasma, and pipette tip into biohazard         waste bin.     -   o. Place the plunger in the syringe and slowly (˜1 ml/second)         depress the plunger until the contents of the syringe have         passed through the filter onto the 7 ml 150K MWCO column.     -   p. Pass entire sample through filter until the 7 ml 150K MWCO         column is full of liquid or bubbles are seen passing through the         filter.     -   q. Discard syringe and attached filter into biohazard waste bin         and tightly cap all 7 ml 150K MWCO columns.

NOTE: Steps k)-q) should be performed inside a biosafety cabinet.

NOTE: If flowthrough from the plasma is not clear and has coloration at any point during concentration, it is likely the column has ruptured and the sample should be discarded. Similarly, if the concentrated plasma volume falls below 100 μl at any point during concentration, the sample should be discarded. In either case, order a new plasma sample and repeat this procedure.

Vesicle Concentration Centrifugation Protocol

-   -   a. Centrifuge 7 ml 150K MWCO columns at 2000×g at 20° C. (16° C.         to 24° C.) for 1 hour. Open centrifuge and check samples to see         if they fall within the following plasma concentrate volume         range:         -   Target Volume: 0.3× Original Plasma Volume (in μl)         -   Minimum Allowable Volume: 100 μl         -   For example: if original plasma volume was 900 μl, Target             Volume would be 270 μl (0.3×900=270).     -   b. During 1 hour spin, prepare 100 mls of 10% bleach in 1 liter         beaker.     -   c. During 1 hour spin, affix one side label to each co-polymer         1.5 ml tube (1 per sample).     -   d. After 1 hour spin, pour the flow-through into 10% bleach.         When beaker is full or all samples have been poured off, pour         down drain.     -   e. Visually inspect sample volume. If plasma concentrate is         above the 8.5 ml graduation on the concentrator tube, continue         to spin plasma sample at 10 minute increments at 2000×g at         20° C. (16° C. to 24° C.) checking volume after each spin until         plasma concentrate is between 8.0 and 8.5 mls.     -   f. Avoid scraping the white filter with the pipette tip during         this step. At the conclusion of the spin, with a p1000 pipette         set to 150 pipette mix slowly on the column a minimum of 6 times         (avoid creating bubbles), and adjust pipette to determine plasma         concentrate volume. If volume is between 100 ul and Target         Volume, transfer concentrated plasma to previously labeled         co-polymer 1.5 ml tube. If the volume is still greater than         Target Volume, repeat step e).     -   g. Record concentrated plasma volume on the Plasma Concentration         Bench Sheet and discard concentrator column in biohazard waste         bin.     -   h. Enter the plasma volume, the concentrate volume, concentrator         lot numbers in the electronic Plasma Concentration Bench Sheet,         save, print a new copy of the bench sheet and affix it to the         original copy.     -   i. Pour ˜45 mls of 1×PBS prepared in sterile molecular grade         water into 50 ml conical tube for use in the next step.     -   j. According to the Plasma Concentration Bench Sheet printed         above, add the appropriate amount of 1×PBS to reconstitute the         sample to the Target Volume.     -   k. Store concentrated plasma sample at 4° C. (2° C. to 8° C.)         overnight in tube rack before running analysis on the subsequent         day. Cover rack with plastic lid and label lid with date and         accession numbers.

Calculations:

-   -   a. Final volume of concentrated plasma sample x=y*0.3, where x         is the final volume of concentrate and y is the initial volume         of plasma.         -   Example: Sample volume is 900 μl. 900 μl*0.3=270 μl final             volume.

References:

-   -   a. Pierce concentrators, 150K MWCO (molecular weight cut off)         7 ml. Part number: 89922 Product Insert.

Example 50 Microsphere Vesicle Analysis from Concentrated Plasma

This Example presents a process for evaluating vesicles concentrated patient plasma samples. The protocols can be used for the analysis of vesicle surface biomarkers in concentrated plasma samples processed as outlined in Example 49.

Equipment, Reagents & Supplies:

Equipment

-   -   a. VWR digital vortex mixer, catalog number 14005-824 (VWR         International, LLC, West Chester, Pa.)     -   b. Boekel Scientific Jitterbug 4, catalog number 270440 (Boekel         Scientific, Feasterville, Pa.)     -   c. Pall life sciences vacuum manifold, catalog number 13157         (Pall Corporation, East Hills, N.Y.)     -   d. Pall life sciences multiwall plate vacuum manifold, catalog         number 5017     -   e. Pall life sciences 1 ml receiver plate spacer block, catalog         number 5014     -   f. Pall life sciences waste drain adapter retainer, catalog         number 5028     -   g. Single channel pipettors: 20 μl, 10 μl, 20 μl, 200 μl, 1000         μl     -   h. Eight channel pipettors: 20 μl, 200 μl     -   i. Electronic eight channel pipette: 1000 μl     -   j. Electronic single channel pipette: 200 μl, 1000 μl     -   k. Serological Pipettor, Pipette Boy, VWR, catalog number         14222-180     -   l. Luminex LX200 Instrument (Luminex Corporation, Austin, Tex.)     -   m. Microplate shaker, VWR, catalog number 12620-926     -   n. VWR MiniFuge Microcentrifuge, VWR, catalog number 93000-196     -   o. Ice Machine, Scotsman, catalog number AFE424 (Scotsman Ice         Systems, Vernon Hills, Ill.)

Reagents

-   -   a. NOTE: Antibody reagents listed below are exemplary         antibodies. To perform a test with alternate capture and/or         detection antibodies, antibodies to the biomarkers of interest         are selected as desired.

Exemplary Capture Antibodies—to be chosen depending on desired test objectives. See Table 45.

TABLE 45 Capture Antibodies Protein Target Vendor Catalog number Clone Type Source CD9 R&D Systems MAB1880 209306 IgG2b Mouse (Minneapolis, MN) CD63 BD Biosciences 556019 H5C6 IgG1 Mouse (San Jose, CA) CD81 BD Biosciences 555675 JS-81 IgG1 Mouse PSMA BioLegend (San 342502 LNI-17 IgG1, κ Mouse Diego, CA) PCSA Millipore MAB4089 5E10 IgG1 Mouse (Temecula, CA) B7H3 BioLegend 135602 MIH35 IgG2a, κ Rat BioLegend 135604 MIH35 IgG2a, κ Rat IL8 GeneTex, Inc. GTX18672 807 IgG1 Mouse (Irvine, CA) GeneTex GTX18649 I8-S2 IgG2b Mouse United States I8430-06A 5D21 IgG1 Mouse Biological (Swampscott, MA) Thermo Scientific OMA1-03346 790128G2 IgG1κ Mouse (Pierce, Rockford, IL) MCP-1 Novus Biologicals NBP1-42360 MNA1 IgG1 Mouse GeneTex GTX18678 S101 IgG1κ Mouse GeneTex GTX18677 S14 IgG1κ Mouse Thermo Scientific MA1-81750 2.2-4A4-1A11 IgG1 Mouse (Pierce) TNF-alpha Thermo Scientific HYB 141-09-02 10D9 IgG1 Mouse (Pierce) Thermo Scientific MA1-21386 CH8820 IgG1 Mouse (Pierce) R&D Systems MAB610 28401 IgG1 Mouse SRVN ProMab Mab-2007128 2H5H2 IgG1 Mouse Biotechnologies, Inc. (Richmond, CA) United States S8500-03L 6A189 IgG1 Mouse Biological Sigma-Aldrich Co. WH0000332M1 5B10 IgG2aκ Mouse (St. Louis, MO) IL-1B Sigma-Aldrich Co. WH0003553M1 2A8 IgG2bκ Mouse Thermo Scientific OMA1-03331 508A4A2 IgG1κ Mouse (Pierce) Thermo Scientific MA1-24785 8516.311 IgG1 Mouse (Pierce) AFP Abcam plc. ab54745 Monoclonal IgGκ Mouse (Cambridge, MA) Abcam ab8201 Polyclonal IgG Rabbit CA-19-9 United States C0075-03 1.B.837 IgG1 Mouse Biological BCNP1 Abcam ab59781 polyclonal IgG Rabbit BANK1 Abcam ab93203 polyclonal IgG Rabbit CDA Abcam ab35251 polyclonal IgG Sheep LAMN United States L1225-25 2Q601 IgG1 Mouse Biological United States L1225-20 2Q592 IgG2a Mouse Biological United States L1225-21 2Q596 IgG1κ Mouse Biological M-CSF R&D Systems MAB616 21113 IgG2A Mouse MIF Sigma-Aldrich Co. WH0004282M1 2A10-4D3 IgG1κ Mouse GeneTex GTX14575 2Ar3 IgG1 Mouse Thermo Scientific MA1-20881 2Ar3 IgG1 Mouse (Pierce) HBD 1 MyBioSource, MBS311954 M11-14b-D10 IgG1 Mouse LLC (San Diego, CA) HBD2 MyBioSource MBS311949 L12-4C-C2 IgG1 Mouse CRMP-2 AbD Serotec AHP1255 Polyclonal IgG Goat (Raleigh, NC) Abcam ab75036 Polyclonal IgG Rabbit PSME3 Abcam ab91540 Polyclonal IgG Rabbit Abcam ab91542 Polyclonal IgG Rabbit MIC1 United States M1199 9E99 IgG1 Mouse Biological Reg IV Abcam ab89917 MM0254-9B21 IgG2 Mouse Trail-R4 R&D systems MAB633 104918 IgG1 Mouse Trail-R2 Thermo Scientific PA1-23497 Polyclonal IgG Rabbit (Pierce) CD44 Novus Biologicals, NBP1-04276 5C10 IgG2b Mouse LLC (Littleton, CO) Thermo Scientific MA1-19277 MEM-263 IgG1 Mouse (Pierce) ALIX United States A1355-64 8J89 IgG1 Mouse Biological Thermo Scientific MA1-83977 3A9 IgG1 Mouse (Pierce) FASL United States F0019-65B 5E501 IgG1 Mouse Biological United States F0019-66V 9i01 IgG1 Mouse Biological L1CAM GeneTex GTX23200 UJ127 IgG1κ Mouse GenWay Biotech, 20-272-193053 UJ181.4 IgG Mouse Inc. (San Diego, CA) CRP United States C7907-05A 3H109 IgG1κ Mouse Biological R&D Systems MAB17071 232007 IgG2b Mouse Abcam ab13426 CRP8 Abcam ab76434 CRP135 R&D Systems MAB1707 232026 IgG2a Mouse DLL4 Abcam ab61031 Monoclonal IgG2a Mouse Cell Signaling 2589 Polyclonal Rabbit Technology, Inc. (Danvers, MA) AURKB Sigma-Aldrich WH0009212M3 6H7 IgG1κ Mouse Novus Biologicals H00009212-M01A 6A6 IgG1κ Mouse AURKA United States A4190-10D 8J339 IgG2b Mouse Biological Thermo Scientific MA1-34566 35C1 IgG2b Mouse (Pierce) SPARC R&D Systems MAB941 122511 IgG1 Mouse United States O8063-08 5E143 IgG1 Mouse Biological PDGFRB Sigma-Aldrich WH0005159M8 4C12 IgG2aκ Mouse R&D Systems MAB1263 PR7212 IgG1 Mouse TFF3 Sigma-Aldrich WH0007033M1 Clone 3D9 IgG1κ Mouse DR3 United States D4012-01B polyclonal IgG Rabbit Biological MACC-1 ProSci 5197 polyclonal IgG Rabbit Incorporated (Poway, CA) MMP7 Novus Biologicals NB300-1000 polyclonal IgG Rabbit TROP2 Santa Cruz sc103908 Clone c12 IgG Goat Biotechnology, Inc. (Santa Cruz, CA) Santa Cruz sc80406 Clone yy01 IgG2a Mouse A33 Santa Cruz sc33014 Clone g20 IgG Goat Santa Cruz sc33012 Clone n15 IgG Goat CXCL12 R&D Systems MAB350 79018 IgG1 Mouse Sigma Aldrich WH0006387M1 1E5 IgG2bκ Mouse GDF15 LifeSpan LS-C89472 Clone 9E99 IgG1 Mouse Biosciences, Inc. (Seattle, WA) ASCA Abcam ab19498 polyclonal IgG Rabbit Abcam ab19731 polyclonal IgG Rabbit Abcam ab25813 polyclonal IgG Goat VEGFA United States V2110-16A clone 5G233 Mouse Biological EphA2 Santa Cruz sc924 Clone c20 Rabbit EGFR BD Biosciences BD555996 Clone EGFR1 Mouse MUC1 Santa Cruz sc7313 Clone VU4H5 Mouse TGM2 Sigma-Aldrich WH0007052M10 Clone 2F4 Mouse TIMP-1 Sigma-Aldrich WH0007076M1 Clone 4D12 Mouse GPCR GPR110 GeneTex GTX70591 Polyclonal Rabbit MMP9 Novus Biologicals NBP1-28617 Clone SB15C Mouse TMEM211 Santa Cruz sc86534 Clone c15 Rabbit UNC93A Santa Cruz sc135539 Clone c13 Rabbit CD66e CEA United States C1300-08 polyclonal Rabbit Biological CD24 Santa Cruz sc19585 Clone sn3 Mouse CD10 BD Biosciences 555373 Clone HI10a Mouse NGAL Santa Cruz sc50350 Clone h130 Rabbit GPR30 Abcam ab12563 Polyclonal Rabbit OPN Santa Cruz sc-73631 Clone lfmb-14 Mouse MUC17 Santa Cruz sc32602 Clone c19 Goat p53 BioLegend 645702 clone do.1 Mouse MUC2 Santa Cruz sc15334 Clone H-300 Rabbit Ncam R&D Systems MAB2408 clone301040 Mouse Tsg 101 Santa Cruz sc-101254 clone Y16J Mouse Epcam R&D Systems MAB 9601 MAB 9601 Mouse

Microplex Microspheres with Conjugated Antibody. Capture antibodies are conjugated to desired microspheres selected from fluorescently-dyed carboxylated MicroPlex® microsphere beads, SeroMAP™ microsphere beads and MagPlex microsphere beads (Luminex Corporation, Austin, Tex.). Conjugation is performed using protocols supplied by the manufacturer. See “SAMPLE PROTOCOL FOR TWO-STEP CARBODIIMIDE COUPLING OF PROTEIN TO CARBOXYLATED MICROSPHERES,” “SAMPLE PROTOCOL FOR CONFIRMATION OF ANTIBODY COUPLING,” and related protocols available online at www.luminexcorp.com/support/protocols/protein.html. Further details are provided in the Examples herein.

Exemplary conjugates are shown below in Table 46. Any appropriate capture antibody can be used for conjugation, e.g., any of those listed above in Table 45 or other antibodies that target an antigen of interest including those listed herein (see, e.g., Tables 5, 6-9, 14).

TABLE 46 Microsphere Conjugated Antibodies Antibody Used to Conjugate* Catalog Antibody Bead Region Vendor Number Antigen Clone 5 Luminex (Austin, L100-C105-04 CD9 209306 TX) 20 Luminex L100-C120-04 CD63 H5C6 24 Luminex L100-C124-04 CD81 JS-81 15 Luminex L100-C115-04 PSMA LNI-17 19 Luminex L100-C119-04 PCSA 5E10 25 Luminex L100-C125-04 B7H3 MIH35 *Conjugated antibody information can be found in the Capture Antibody table above.

Detection Antibodies—various labels can be used. Exemplary antibodies to the tretrapannins CD9, CD63 and CD81 are shown. Antibodies to other biomarkers, e.g., general vesicle biomarkers, cell of origin specific biomarkers, or disease biomarkers, can be used as desired. See Table 47.

TABLE 47 Detection Antibodies Custom Conjugation Size of of Catalog Custom Protein Vendor number Conjugation Clone Type Source Label* CD9 BD 624048 4 mg M-L13 IgG1 Mouse PE Biosciences CD63 BD 624048 4 mg H5C6 IgG1 Mouse PE Biosciences CD81 BD 624048 4 mg JS-81 IgG1 Mouse PE Biosciences *Phycoerythrin

-   -   a. Phosphate buffered saline (PBS) with BSA, pH 7.4, Sigma,         catalog number P3688-10PAK (Sigma, Saint Louis, Mo., part of         Sigma-Aldrich, Inc.)     -   b. Starting Block Blocking Buffer in PBS, Thermo Scientific,         catalog number 37538 (Thermo Scientific, Part of Thermo Fisher         Scientific, Waltham, Mass.)     -   c. PBS-BN (PBS, 1% BSA, pH 7.4 Sigma Cat# P3688, 0.05% Sodium         Azide, Sigma, catalog number S8032     -   d. Sterile Molecular Grade Water (DNase and RNase Free, 0.1 μM         filtered), Sigma, catalog number W4502     -   e. VCaP microvesicles (2.14 μg/μl)     -   f. Normal Male Plasma, Lot#55-24482-042610 (Innovative Research,         sample 55-24482), used for VCaP control creation

Supplies

-   -   a. USA Scientific TempAssure PCR 8-tube strip, catalog number         1402-2908 (USA Scientific, Inc., Ocala, Fla.)     -   b. Millipore Multiscreen HV Luminex filter plates, 0.45 microM,         clear, styrene, Millipore catalog number MSBVN1250 (Millipore,         Billerica, Mass.)     -   c. USA Scientific co-polymer 1.5 ml non-binding tubes, catalog         number 1415-2500     -   d. USA Scientific TempPlate Sealing foil, catalog number         2923-0110     -   e. Disposable filtered and sterile pipette tips, DNase, RNase         and pyrogen free     -   f. 1L glass bottles, VWR, catalog number 89000-240 (VWR         International, LLC, West Chester, Pa.)     -   g. 250 ml glass bottles, VWR, catalog number 89000-236     -   h. Stir bars, medium, VWR, catalog number 58948-218     -   i. Ice bucket, Fisher, catalog number 02-591-46 (Fisher         Scientific, Part of Thermo Fisher Scientific, Pittsburgh, Pa.)     -   j. 96 well Falcon Plate, VWR, catalog number 62406-321     -   k. 1L graduated cylinder, Fisher, catalog number 03-007-36     -   l. Plate racks, Fisher, catalog number 05-541-55     -   m. Tube racks, Fisher, catalog number 05-541-38     -   n. 4-way racks, Fisher, catalog number 03-448-17     -   o. 15 ml conical, VWR, catalog number 21008-918     -   p. Reagent reservoirs, VWR, catalog number 89094-662     -   q. 10/20 ul filtered pipet tips, Rainin, catalog number GP-L10F         (Rainin Instrument, LLC, Oakland, Calif., a METTLER TOLEDO         Company)     -   r. 200 ul filtered pipet tips, Rainin, catalog number GP-L200F     -   s. 1000 ul filtered pipet tips, Rainin, catalog number GP-L1000F     -   t. 1000 ul non-filtered pipet tips, Rainin, catalog number         GPS-L1000     -   u. Aluminum Foil, Fisher, 01-231-100     -   v. Personal protective equipment     -   w. Master Plan Template Spreadsheet (tracking spreadsheet)

Quality Control:

Assay Controls

Assay controls consist of microvesicles from the VCaP cell line. VCaP is a human epithelial cell line established in 1997 from a vertebral bone metastasis from a 59 year old Caucasian male patient with hormone refractory prostate cancer. It was passaged as xenografts in mice then cultured in vitro. The VCaP cell line is androgen sensitive and produces vesicles. See Korenchuk, S., et al., VCaP, a cell-based model system of human prostate cancer. In Vivo, 2001. 15(2): p. 163-68; Jansen, F. H., et al., Exosomal secretion of cytoplasmic prostate cancer xenograft-derived proteins. Mol Cell Proteomics, 2009. 8(6): p. 1192-205.

A triplicate set of VCaP microvesicle (MVS) High and Blank controls are run on each plate to verify (1) bead master mix performance, (2) individual run technical specifications, and (3) detection antibody performance. The VCaP MVS High control consists of 0.5 mg/ml purified VCaP microvesicles diluted into normal male plasma. The VCaP MVS Blank control consists of 0 mg/ml purified VCaP microvesicles (i.e. no purified microvesicles) in a PBS background.

A run is considered valid if the average signal (High VCaP MVS control) to average background (Blank VCaP MVS control) ratio is at least 10-fold above background. A signal of this magnitude indicates that each conjugated capture bead has sufficient sample binding capabilities and that a technically intact run has been performed.

If a run doesn't meet the established metric for the VCaP MVS Controls, then the entire run is repeated. The repeated run will consist of the VCaP MVS controls and an additional plasma concentration of the patient plasma (see Plasma Concentration; Example above). The run is repeated up to two times depending upon number of specimens received. If the run fails the final time, the samples are reported as failed without obtaining a valid result.

Internal Controls

The tetraspanin capture antibodies (CD9, CD63, and CD81) serve as an internal control for adequacy of each sample tested. The average MFI (median fluorescent intensity) is calculated for the three tetraspanin capture antibodies. If the average combined MFI value is greater than 500, then the sample is considered to have a sufficient microvesicle concentration for further testing.

If a sample doesn't meet the established metric for the tetraspanin capture antibodies, then the sample is retested. The repeated run consists of the VCaP MVS and an additional plasma concentration of the patient plasma (see Plasma Concentration, Example above). The run will be repeated up to two more times provided there are additional aliquots of patient plasma. If the repeated run fails the final time, then the specimen is reported as Non-evaluable without obtaining a valid result.

Limitations:

If patient plasma is collected or stored improperly, then the vesicles therein may be degraded or have their protein content altered. Degradation of vesicles may lead to aggregation and false protein expression readings, leading to indeterminate or erroneous results.

Procedure:

All steps use filtered tips for pipettes with the exception of the wash steps.

-   -   a. Open appropriate Master Plan Template Spreadsheet and on the         Lot Info tab fill in any empty yellow cells with appropriate         information. Save the file.     -   b. Select the Work Bench Sheet tab (tracking and instruction         worksheet in the Master Plan Template Spreadsheet), and print a         hard copy for use on the bench.     -   c. Remove concentrated plasma samples from the previous day from         refrigerator and place on bench. See Example above for         concentrated plasma preparation.     -   d. Prepare VCaP MVS controls according to recipe on Work Bench         Sheet.         -   i. Fill ice bucket with flaked ice.         -   ii. Remove 1 tube of VCaP MVS Pool (pooled control sample)             and 1 tube of Normal Plasma per plate from −80° C. (−65° C.             to −85° C.) and thaw on ice.         -   iii. Vortex VCaP MVS Pool for 10 seconds at 1600 rpm.         -   iv. Prepare 0.5 ug/ul VCaP Control (refer to Work Bench             Sheet). Normal Plasma tube contains 11.1 μl of normal             plasma; VCaP tube contains 5 μl VCaP. Pipette appropriate             amount of VCaP MVS Pool (orange tube) into Normal Plasma             tube (purple tube) and pipette mix 5 times.         -   v. Place tube in plate rack and incubate for 1 hour at             37° C. (35° C. to 39° C.) in the Jitterbug with shaking at             550 rpm.     -   e. While controls are incubating, prepare Sample Bead Mix.         -   i. Label a new 1.5 ml co-polymer tube with date, initials,             and “Sample Bead Mix.”         -   ii. Add Starting Block and Sample Bead Mix to labeled tube             (refer to Work Bench Sheet).         -   iii. Vortex for 5 seconds on VWR digital vortex at 1600 rpm.         -   iv. Wrap in aluminum foil and incubate on bench top for a             minimum of 10 min.     -   f. While controls are incubating, retrieve the necessary number         of 8-tube strips from storage container based on the plate map         on the Work Bench Sheet. Each strip represents 1 column on the         plate map (maximum number of 8-tube strips per plate is 12).     -   g. Arrange 8-tube strips vertically in plate rack in every other         column and cap each tube. Label tops of tubes starting with 1 at         the top left position and number sequentially top to bottom then         left to right. For example, strips 1-6 are labeled 1-48 in         FIG. 107. Strips 7-12 would be labeled 49-96 on a second plate         rack.     -   h. Transfer 50 μl of each concentrated plasma sample to the         8-tube strips in triplicate according to the plate map on the         Work Bench Sheet.         -   i. Open all tubes except 1, 2, 9, 10, 17, and 18; these are             reserved for the VCaP High and Blank controls.         -   ii. Vortex each concentrated plasma sample tube for at least             5 seconds on digital vortex at 1600 rpm to thoroughly mix             plasma directly before aliquoting into 8-tube strips.         -   iii. Using a p200 pipette, transfer sample to 8-tube strips             closing caps after each addition of a sample.             -   1. Pre-wet each new pipette tip by drawing up 50 μl                 concentrated plasma into pipette tip and dispensing back                 into sample tube once.             -   2. Using the same pipette tip, draw up another 50 μl                 concentrated plasma and dispense into correct tube                 making sure to position pipette tip at the bottom of the                 tube while dispensing. Bring pipette tip straight out of                 tube being careful not to drag pipette tip up the side                 of the tube.             -   3. Repeat step 2) above twice until all three tubes for                 that sample contain 50 μl concentrated plasma. The same                 tip can be used for all three aliquots of the same                 sample, but tip should be changed between different                 samples.     -   i. After 1 hour VCaP Control incubation, pipette 4 μl of the 0.5         μg/μl VCaP Control into tubes 1, 9, and 17 using a p20 pipette.     -   j. Pipette 4 μl of 1×PBS into tubes 2, 10, and 18.     -   k. Add bead mixture to all samples.         -   i. Open all tubes.         -   ii. Vortex Sample Bead Mix for 5 seconds on digital vortex             at 1600 rpm. Repeat this vortex step prior to each             successive pipette aspiration.         -   iii. Using a 200 μl electronic repeater pipette, add 4 μl of             bead mixture to each sample tube, including control tubes,             closing caps after each addition of beads.         -   iv. Perform a quick 1 second spin in a mini-galaxy             centrifuge of the 8-tube strips to collect all liquid at the             bottom of the tube.         -   v. Incubate in 37° C. (35° C. to 39° C.) Jitterbug at 550             rpm for 2 hours.         -   vi. Any excess beads may be wrapped in aluminum foil and             kept at 4° C. (2° C. to 8° C.) overnight for use as overage             the following day. This usage of leftover beads may continue             for one week, but each Monday any old beads should be             discarded in biohazard waste bin.     -   1. During 2 hour incubation, prepare Detector Antibodies.         -   i. Label 15 ml conical tube with date, initials and             “Detector Antibodies.”         -   ii. Add PBS-BN to 15 ml conical (refer to Work Bench Sheet             for volume).         -   iii. Add CD9, CD81, and CD63 to PBS-BN (refer to Work Bench             Sheet for volumes).         -   iv. Vortex for 5 seconds on VWR digital vortex at 1600 rpm.         -   v. Wrap in aluminum foil and place in 4-way rack until use.     -   m. Fill a disposable reservoir with PBS-BN.     -   n. If there are less than 23 samples on plate, cut a foil seal         with scissors to cover any empty columns and stick over empty         columns on plate.     -   o. During the following steps, pipette tips should never touch         the bottom of the filter plate wells. Always touch tips to the         side of the wells. Also, the vacuum should always operate         between 3 inches and 5 inches Hg. Plates should only be on         vacuum manifold during aspirations; during all other steps plate         should be placed on bench top.     -   p. Using a 1000 ul electronic multichannel pipette and 1000 ul         non-filtered tips, pre-wet a 1.2 μm Millipore filter plate with         100 μl/well of PBS-BN and aspirate by vacuum manifold.     -   q. Press vacuum release button before removing plate from         manifold. Blot bottom of plate dry on a clean paper towel.     -   r. Using a p200 multichannel pipette, add 150 μl of PBS-BN to         each well of the plate.     -   s. After 2 hour incubation, remove samples from Jitterbug and         perform a quick 1 second spin in a mini-galaxy centrifuge.     -   t. Transfer the incubated samples to the Millipore filter plate         following the plate map on the Work Bench Sheet.         -   i. Working left to right across the plate (columns 1-12)             remove one 8-tube strip at a time from plate rack and place             in an empty plate rack. Verify that 8-tube strips are used             in chronological order by double-checking that the numbers             written on the caps are in proper order and orientation.         -   ii. Using a p20 multichannel pipette, transfer the two             controls in the 8-tube strip to the proper wells on the             filter plate. Pipette mix 5 times during aspiration to             ensure that all beads are in solution before dispensing in             filter plate, pipette mixing twice in the PBS-BN.         -   iii. Using a p200 multichannel pipette, transfer all plasma             samples to the filter plate.         -   iv. Concentrated plasma can be extremely viscous, so slowly             pipette mix each sample 5 times ensuring that plasma is             travelling up and down inside the pipette tip. If a sample             is not travelling, increase number of pipette mixes until             each sample has been mixed at least 5 times. Transfer             samples to filter plate pipette mixing twice in the PBS-BN.         -   v. After each 8-tube strip is empty, verify that all             contents are gone before discarding strip in biohazard waste             bin.         -   vi. If any liquid remains in any of the tubes, repeat steps             i)-iv) above.         -   vii. Continue steps i)-v) above until all samples have been             added to the filter plate.     -   u. If at any point during the steps below any well clogs but         other wells are aspirated, continue constant vacuum for 5         seconds. If some sample is still clogged and not moving through         the filter, mark that well(s) on the plate (with a marker) and         on the Work Bench Sheet. Aspirate liquid out of well(s) using a         p1000 pipette and leave that well(s) empty during all successive         steps.     -   v. Aspirate the supernatant by vacuum manifold slowly. Press         vacuum release button before removing plate from manifold. Blot         bottom of plate dry on a clean paper towel.     -   w. Using a p1000 electronic multichannel pipette and 1000 ul         NON-FILTERED tips, wash each well with 200 μl of PBS-BN,         aspirate, press vacuum release button, then remove plate from         manifold and thoroughly blot bottom of plate on a clean paper         towel.     -   x. Repeat previous step for a total of 2 washes with 200 ul         PBS-BN per wash.     -   y. Using a p200 multichannel pipette, add 50 μl of PBS-BN to         each well.     -   z. Using a p1000 electronic single channel pipette, add 50 μl of         the diluted detection antibody (from above) to each well.         -   i. Any excess detection antibody may be wrapped in aluminum             foil and kept at 4° C. (2° C. to 8° C.) overnight for use as             overage the following day. This usage of leftover detection             antibodies may continue for one week, but any old detection             antibodies should be discarded in a biohazard waste bin.     -   aa. Cover the filter plate using a foil plate seal. Gently seal         the foil along the outside perimeter, being careful not to         create positive pressure within the wells as this will force         liquid out the bottom of the filter.     -   bb. Incubate at 25° C. (22° C. to 27° C.) on the Jitterbug at         550 rpm for 1 hour.     -   cc. During 1 hour incubation refer to Luminex Maintenance and         Calibration SOP (MA-25-0009) and perform all necessary         maintenance and/or calibrations on the Luminex bead reader         machine.     -   dd. After maintenance and/or calibrations are complete, enter         plate information in Luminex software.         -   i. Open xPONENT 3.1 software and log in.         -   ii. Click the Batches tab.             -   Under Batch Name, enter the ID of the plate which is                 found near the top of the Work Bench Sheet but add an                 additional underscore and 1 at the end.                 -   1. ID: 20100915_SampleV1_PME_(—)1         -   iii. This later denotes the upload number into the data             store. For example:             -   1. Batch Name: 20100915_SampleV1_PME_(—)1_(—)1         -   iv. Click on Create a New Batch from an Existing Protocol             and select the desired protocol.         -   v. Click Next.         -   vi. Highlight wells containing samples and controls by             clicking and dragging on the plate map.         -   vii. Click the Unknown button below the plate map.         -   viii. On the right side of the screen click the Import List             button and navigate to the appropriate exported text file,             select it, then click Open.     -   ee. After 1 hour incubation of samples, remove filter plate from         Jitterbug, remove foil seal and aspirate the supernatant by         vacuum manifold. Press vacuum release button then remove plate         from manifold and thoroughly blot bottom of plate on a clean         paper towel.     -   ff. Using a p1000 electronic multichannel pipette and 1000 ul         non-filtered tips, wash each well with 100 μl of PBS-BN,         aspirate, press vacuum release button, then remove plate from         manifold and thoroughly blot bottom of plate on a clean paper         towel.     -   gg. Repeat previous step for a total of 2 washes with 100 ul         PBS-BN per wash.     -   hh. Using a p200 multichannel pipette, add 100 μl of PBS-BN to         each well.     -   ii. Cover the filter plate using a foil plate seal. Gently seal         the foil along the outside perimeter.     -   jj. Place plate on VWR microplate shaker for 20 seconds at 950         rpm. Analyze plate on Luminex 200 machine.         -   i. Retrieve plate from shaker and remove foil seal.         -   ii. Click the Eject button at the bottom of the screen.         -   iii. Place plate on drawer (A1 goes in top left corner).         -   iv. Click the Retract button at the bottom of the screen.         -   v. Click the Run Batch button in the lower right corner of             the screen.         -   vi. Click OK in the pop-up window.     -   kk. At the conclusion of the run, go to the Results tab, select         Saved Batches on the left, highlight the run and click on Exp         Results at the bottom of the screen to export a .csv     -   ll. Save the .csv file to the appropriate network server         location.     -   mm. Log into the data analysis software, go to the Lab Queues         tab and select Import Results near the top right corner.     -   nn. Click Browse and navigate to the .csv on the clinical drive         and click Open.     -   oo. Verify that the data in the data analysis software is         consistent with that exported from the Luminex 200 machine.

Example 51 Analysis of Prostate Cancer (PCa) Vesicles Using Multiplex Assays

In this example, plasma samples from patients with prostate cancer (PCa) or without PCa (normals) are analyzed according to the general procedure outlined in Example 27. Plasma is prepared according to the protocol of Example 49 and multiplex analysis is performed as in Example 50. Capture antibodies to the vesicle surface antigen proteins in Table 48 were used to screen for biomarkers that detect PCa.

TABLE 48 PCa Capture Antibodies Protein Catalog Target Antibody Vendor Number(s) SPB Anti surfactant protein-B antibody US Biological S8401-02 IL-8 Anti interleukin 8 antibody US biological I8430-06A SPC Anti surfactant protein-C antibody US Biological U2575-03 IL8 Anti Interleukin 8 antibody Thermo scientific OMA1-03346 pierce MUC1 MUC 1 aptamer 2_3′ amino.mod IDT 55403591 seq1 TFF3 Anti Trefoil factor 3 (intestinal) antibody Sigma-Aldrich WH0007033M1 TF (FL- Anti tissue factor (coagulation factor III) antibody Santacruz sc-20160 295) MUC1 MUC 1 aptamer 4_3′ amino.mod IDT 55403593 seq3 PGP9.5 Anti protein G product 9 antibody Genway 20-002-35062 MCP-1 Anti monocyte chemoattractant antibody Novus Biologicals NBP1-42360 CD9 Anti Cluster of Differentiation 9 antibody Novus biologicals NBP1-28363 CD9 Anti Cluster of differentiation 9 antibody R&D systems MAB1880 MCP-1 Anti monocyte chemoattractant antibody Genetex GTX18678 MS4A1 Anti membrane spanning 4A1 antibody Sigma WH0000931M1 5c11 EphA2 Anti Ephrin-A receptor antibody Santa Cruz sc924 HSP70 Anti heat shock protein antibody Biolegend 648002 TNF- Anti tumor necrosis factor-alpha antibody Thermo scientific HYB 141-09-02 alpha pierce TIMP2 Anti tissue inhibitor of metallo proteinase 2 antibody Genetex GTX48556 GAL3 Anti Galactose metabolism regulator 3 antibody Santa Cruz sc-32790 TNF- Anti tumor necrosis factor-alpha antibody Thermo scientific MA1-21386 alpha pierce SRVN Anti survivin antibody US biologicals S8500-03L INSIG-2 Anti insulin induced gene 2 antibody Santa Cruz sc-66936 PTEN Anti phosphatase and tensin homolog antibody Sigma Aldrich WH0005728M1- 100UG SRVN Anti survivin antibody Sigma Aldrich WH0000332M1 MIS RII Anti Mnllerian inhibiting substance receptor II R&D AF4749 antibody HER2 Anti Human Epidermal growth factor Receptor 2 R&D MAB1129 (ErbB2) antibody EGFR Anti epidermal growth factor antibody BD biosciences 555996 N-gal Anti Neutrophil gelatinase-associated lipocalin Santa Cruz sc-18698 antibody IL-1B Anti Interleukin-1B antibody Sigma Aldrich WH0003553M1 ER Anti estrogen receptor antibody US Biological E3564-89 SPP1 Anti-SPP1 Sigma WH0006696M1 iC3b Anti human inactive complement component 3b Thermo MA1-82814 antibody AFP Anti alpha fetal protein antibody Abcam ab54745 PSMA Anti prostate-specific membrane antibody Biolegend 342502B PSMA Anti Prostate-specific membrane antibody Genetex GTX19071 PSMA Anti Prostate-specific membrane antibody 342502 AFP Anti alpha fetal protein antibody Abcam ab8201 KLK2 Anti kallikrein-related peptidase 2 antibody Novus Biologicals H00003817-M03 PR (B) Anti progesterone R antibody R&D PP-H5344-00 MRP8 Anti Migration inhibitory factor-related protein 8 US Biological M4688-36A antibody CA-19-9 Anti carbohydrate 19-9 antibody US Biological C0075-13B BCNP1 Anti B-cell novel protein1 antibody abcam ab59781 BANK1 Anti B-cell scaffold protein with ankyrin repeats 1 abcam ab93203 antibody PCSA Anti-Prostate Cell Surface antibody Millipore MAB4089 PCSA Anti-Prostate Cell Surface antibody In house CD63 Anti Cluster of Differentiation 63 antibody R&D systems MAB5048 CD63 Anti Cluster of differentiation 63 antibody BD biosciences 556019 B7H4 Anti immune cosmitulatory protein antibody US Biological B0000-35A CDA Anti cytidine deaminase antibody abcam ab35251 MUC1 Anti Mucin 1, cell surface associated protein antibody Santa Cruz sc7313 TGM2 Anti Transglutaminase-2 antibody Sigma-Aldrich WH0007052M10 DLL4 Anti Delta like protein 4 antibody Santa Cruz sc-18639 CD81 Anti Cluster of Differentiation 81 antibody Sigma Aldrich WH0000975M1 CD81 Anti Cluster of differentiation 81 antibody BD biosciences 555675 B7H3 Anti Cluster of Differentiation 276(B7 homolog 3) R&D systems MAB1027 antibody B7H3 Anti Cluster of differentiation 276 antibody BioLegend 135602 LAMN Anti Laminin antibody US Biological L1225-25 HER 3 Anti Human Epidermal growth factor Receptor 3 US Biological E3451-36A (ErbB3) antibody CRP Anti c-reactive protein antibody abcam ab76434 MART-1 Anti Melanoma Antigen Recognized by T-cells 1 US Biological M2410 antibody LAMN Anti Laminin antibody US Biological L1225-20 M-CSF Anti Macrophage colony-stimulating factor antibody R&D systems MAB616 PSA Anti Prostate specific antibody My bioscource MBS312739 MFG-E8 Anti milk fat globule-EGF factor 8 protein antibody R&D systems MAB27671 CD46 Anti Cluster of differentiation 46 antibody R&D systems MAB2005 STAT 3 Anti Signal transducer and activator of transcription 3 US Biological S7971-01M antibody MIF Anti macrophage migration inhibihitory factor Genetex GTX14575 antibody TIMP-1 Anti tissue inhibitor of metallo proteinase-1 antibody Sino biological 10934-MM02 Inc MACC-1 Anti Metastatis associated in colon cancer-1 antibody ProSciInc 5197 PSA Anti prostate specific antigen antibody R&D MAB13442 ALP Anti Apolipoprotein J antibody Novus Biologicals H00001191-M02 CRP Anti c-reactive protein antibody Abcam ab13426 HBD2 Anti human beta defensin 2 antibody My biosource MBS311949 TrKB Anti tyrosine Kinase B antibody Novus biologicals NB100-92063 (poly) MMP26 Anti matrix metallo proteinase 26 antibody Novus Biologicals H00056547-M02 AnnV Anti-AnnexinV antibody Abcam ab54775 CD24 Anti Cluster of differntiation 24 antibody (Heat BD biosciences bd 555426 Stable antigen) VEGF A Anti Vascular endothelial growth factor A antibody US Biological V2110-05D PCSA Anti prostate cell surface antibody Novus Biologicals H00008000-M03 TIMP-1 Anti Tissue inhibitor of metallo proteinase-1 antibody Sigma-Aldrich WH0007076M1 IL6 Unc Anti interleukin 6 unconjugated antibody Invitrogen AHC0762 CRMP-2 Anti collapsin response mediator protein 2 antibody Abcam ab75036 IL 6 Unc Anti human interleukin 6 unconjugated antibody Invitrogen AHC0762 OPG Anti osteoprotegerin antibody Biovendor RD182003110- 13 PSME3 Anti proteasome activator complex subunit 3 Abcam ab91540 antibody PBP Anti Prostatic binding protein antibody Novus Biologicals H00005037-M01 PSME3 Anti proteasome activator complex subunit 3 Abcam ab91542 antibody IL6R Anti interleukin 6 receptor antibody Sigma aldrich WH0003570M1 CD59(M Anti Cluster of differentiation 59 (MEM-43) antibody Genetex GTX74620 EM-43) PIM1 Anti proviral integration site antibody Novus Biologicals H00005292-M08 GPCR Anti G-protein coupled receptor antibody (vendor GeneTex GTX70593 replacement) EphA2 Anti Ephrin-A receptor 2 antibody Santa Cruz sc-10746 (H-77) MIC1 Anti macrophage inhibitory cytokine antibody US Biologicals M1199 Reg IV Anti Regenerating islet-derived family, member 4 Abcam ab89917 antibody MMP9 Anti Matrixmetallo Proteinase 9 antibody Novus biologicals NBP1-28617 PRL Anti prolactin Monoclonal antibody Thermo Scientific MA1-10597 Pierce Trail-R4 Anti TNF-related apoptosis-inducing ligand receptor R&D systems MAB633 4 antibody EphA2 Anti Ephrin-A receptor 2 antibody Santa Cruz sc101377 Trail-R2 Anti TNF-related apoptosis-inducing ligand receptor Thermo scientific PA1-23497 2 antibody pierce STEAP1 Anti Six Transmembrane Epithelial Antigene of the US Biological S7500-02 Prostate 1 antibody PA2G4 Anti Proliferation-associated protein 2G4 antibody Sigma aldrich SAB2101707 MMP7 Anti Matrix metallo Proteinase 7 antibody Novus biologicals NB300-1000 TMEM211 Anti Tumor Microenvironment of Metastasis 211 Santa Cruz sc86534 antibody EZH2 Anti Histone-lysine N-methyltransferase antibody Sigma aldrich WH0002146M1 TROP2 Anti human trophoblast cell-surface antibody Santa Cruz sc103908 TMPRSS2 Anti Transmembrane protease, serine 2 antibody Abcam ab92323 EZH2 Anti Histone-lysine N-methyltransferase antibody ABD serotec MCA4898Z PCSA Anti Prostate specific antibody Novus Biologicals NB100-66506 CD44 Anti Cluster of differntiation 44 antibody Novus Biologicals NBP1-04276 SCRN1 Anti secrin-1 antibody Sigma-Aldrich HPA024517 CD44 Anti Cluster of differntiation 44 antibody Thermo scientific MA1-19277 pierce RUNX2 Anti runt-related transcription factor 2 antibody Sigma aldrich WH0000860M1 CD1.1 Anti cyclin D1 antibody GTX26152 22995 EphA2 Anti Ephrin-A receptor 2 antibody Santa Cruz sc135658 TWEAK Anti Tumor necrosis factor like weak inducer of US biological T9185-01 apoptosis ALIX Anti Apoptotic linked gene product 2 Interacting US Biologicals A1355-64 Protein X antibody SERPINB3 Anti serpin peptidase inhibitor, clade B member 3 Sigma aldrich WH0006317M1 antibody ALIX Anti Apoptotic linked gene product 2 Interacting Thermo scientific MA1-83977 Protein X antibody pierce Trop2 Anti human trophoblast cell-surface antibody Santa Cruz sc80406 CDAC11a2 Anti Cytidine Deaminase antibody Sigma WH0081602M1 FASL Anti human Fas Ligand antibody US Biologicals F0019-65B BCA-225 Anti breast cancer antigen 225 antibody US Biological B0395-10B EGFR Anti epidermal growth factor antibody R&D AF231 UNC93A Anti unc 3 homolog A antibody Santa Cruz sc135539 FASL Anti human Fas Ligand antibody US Biologicals F0019-66V ALA Anti serum amyloid A antibody abcam ab18713 DR3 Anti death receptor 3 (apoptosis inducing) antibody US Biological D4012-01B BRCA Anti breast cancer gene antibody US Biological B2708-06 L1CAM Anti L1 cell adhesion molecule antibody Genway 20-272-193053 UNC93A Anti unc 93 homolog A antibody Santa Cruz sc135541 APC Anti adenomatous polyposis antibody US biological A2298-70A CRP Anti c-reactive protein antibody US Biologicals C7907-05A CRP Anti c-reactive protein antibody R&D systems MAB17071 CA125 Anti carbohydrate antigen 125 antibody US Biological C0050-01D (MUC16) A33 Anti glyco protein a33 antibody Santa Cruz sc33014 MMG Anti mammaglobin antibody Santa Cruz sc-48328 CD174 Anti Cluster of differentiation174 antibody US Biological L2056 (Lewis y) DLL4 Anti Delta like protein 4 antibody Abcam ab61031 a33 n15 Anti glyco protein a33 antibody Santa Cruz sc-33012 MUC1 MUC 1 aptamer 11_5′ amino.mod IDT 55403589 seq11A CD66e Anti Carcinoembryonic antigen (CEA, CD66e) US biological C1300-08 CEA DLL4 Anti Delta like protein 4 antibody Cell signaling 2589 technology NPGP/NPFF2 Anti Neuropeptide FF receptor 2 antibody Santa Cruz sc-46206 TIMP1 Anti Tissue inhibitor of metallo proteinase-1 antibody US Biological T5580-08B AURKB Anti Aurora Bkinase (serine/threonine-protein kinase Sigma Aldrich WH0009212M3 6) antibody CD24 Anti Cluster of differntiation 24 antibody (Heat Santa Cruz sc19585 Stable antigen) TIMP1 Anti tissue inhibitor of metallo proteinase 1 antibody R&D systems MAB970 AURKB Anti Aurora Bkinase (serine/threonine-protein kinase Novus Biologicals H00009212- 6) antibody M01A SPDEF Anti SAM pointed domain containing ets Novus Biologicals H00025803-M01 transcription factor antibody C-erbB2 Anti c-erb2 antibody BD Biosciences 610161 CD10 Anti Cluster of differntiation 10 antibody (Heat BD Pharmingen 555373 Stable antigen) AURKA Anti Aurora A kinase (serine/threonine-protein kinase US Biologicals A4190-10D 6) antibody BDNF us Anti Brain derived neutrotrophic factor antibody US Bio B2700-02D bio AURKA Anti Aurora A kinase (serine/threonine-protein kinase Thermo scientific MA1-34566 6) antibody pierce FRT c.f23 Anti Ferritin antibody Santa Cruz sc-51887 FRT Anti Ferritin (heavy chain) antibody Santa Cruz sc-51888 N-GAL Anti Neutrophil gelatinase-associated lipocalin Santa Cruz sc18695 antibody SPARC Anti Secreted protein acidic antibody rich in cysteine R&D systems MAB941 antibody CD73 Anti Secreted protein acidic antibody rich in cysteine R&D systems MAB5795 antibody seprase Anti seprase antibody R&D MAB3715 NGAL Anti Neutrophil gelatinase-associated lipocalin Santa Cruz sc50350 antibody Epcam Anti Epithelial cellular adhesion molecule antibody R&D systems MAB 9601 PDGFRB Anti platelet-derived growth factor Sigma Aldrich WH0005159M8 receptor, beta, subunit antibody CEA Anti carcinoembryogenic antibody US Biological C1300-01B NGAL Anti Neutrophil gelatinase-associated lipocalin Santa Cruz sc59622 antibody GPR30 Anti G-protein receptor antibody GeneTex GTX100001 PDGFRB Anti platelet-derived growth factor R&D systems MAB1263 receptor, beta, subunit antibody MUC17 Anti Mucin 17, cell surface associated protein Santa Cruz sc32600 antibody CYFRA21-1 Anti cytokeratin 19 fragment antibody MedixMab 102221 C-Erb2 Anti c-erb2 antibody US biological C00026-11NX ASCA Anti saccharomyces cerevisiae antibody abcam ab19731 ASCA Anti saccharomyces cerevisiae antibody abcam ab19498 p53 Anti tumor protein 53 antibody BioLegend 645802 OPN Anti osteopontin antibody Santa Cruz sc-21742 MAPK4 Anti mitogen activated protein kinase 4 antibody Sigma-Aldrich WH0005596M2 C-Bir Anti Flagellin antibody abcam ab93713 LDH Anti lactate dehydrogenase antibody US Bio L1011-12H OPN Anti osteopontin antibody Santa Cruz sc-73631 ASPH Anti Aspartyl/asparaginyl β-hydroxylase(DO1 P) Novus H00000444- (D01P) antibody D01P HSP Anti heat shock protein antibody US Bio H1830-94G OPN Anti osteopontin antibody (replacement for OST zz09 R&D systems MAB14332 santacruz sc-80262) OPN Anti osteopontin antibody Santa Cruz sc-80262 ASPH Anti Aspartyl/asparaginyl β-hydroxylase(DO3) Novus H00000444-D03 (D03) antibody ASCA Anti saccharomyces cerevisiae antibody abcam ab25813 CXCL12 Anti Chemokine (C—X—C motif) ligand 12 antibody Sigma Aldrich WH0006387M1 ASPH Anti Aspartyl/asparaginyl β-hydroxylase(G-20) Santa Cruz sc-33367 (G-20) antibody p53 Anti tumor protein 53 antibody BioLegend 628202 MUC17 Anti Mucin 17, cell surface associated protein Santa Cruz sc32602 antibody OPN Anti osteopontin antibody R&D MAB1433 HBD 1 Anti human beta defensin 1 antibody My biosource MBS311954 CRMP-2 Anti collapsin response mediator protein 2 antibody AbD Serotec AHP1255 Trop2 Anti human trophoblast cell-surface antibody Santa Cruz sc103909 OPN Anti osteopontin antibody R&D MAB14332 hVEGFR2 Anti Vascular Endothelial Growth Factor Receptor 2 R&D systems MAB3572 antibody p53 Anti tumor protein 53 antibody BioLegend 645702 OPN Anti osteopontin antibody R&D MAB14331 ASPH Anti Aspartyl/asparaginyl β-hydroxylase(H-300) Santa Cruz sc-66939 (H-300) antibody MUC2 Anti Mucin 2, cell surface associated protein antibody Santa Cruz sc15334 Muc2 Anti mucin 2 antibody Santa Cruz sc-15334 Ncam Anti-hNCAM/CD56 antibody R&D MAB2408 CXCL12 Anti Chemokine (C—X—C motif) ligand 12 antibody R&D systems MAB350 CD10 Anti Cluster of differntiation 10 antibody Santa Cruz sc19993 ASPH Anti Aspartyl/asparaginyl β-hydroxylase(A10) Santa Cruz sc-271391 (A-10) antibody HAP Anti haptoglobin antibody USBIO H1820-05 CRP Anti c-reactive protein antibody US Bio C7907-05A ASPH Anti Aspartyl/asparaginyl β-hydroxylase(666-680) Sigma-Aldrich A7110 (666-680) antibody Gro-alpha Anti Human gro alpha antibody GeneTex GTX10376 ErbB4 Anti Human Epidermal growth factor Receptor 4 US Biological E3451-40F antibody Tsg 101 Anti tumor susceptibility gene 101 antibody Santacruz sc-101254 GDF15 Anti Growth differentiation factor 15 antibody Life span LS-C89472 biosciences ASPH Anti Aspartyl/asparaginyl β-hydroxylase(246-260) Sigma-Aldrich A6985 (246-260) antibody

As indicated in Table 48, multiple antibodies to a single target biomarker are tested as available and as desired. This allows for assessing vesicle capture using different surface epitopes to determine which provide the desired performance for detecting PCa.

Example 52 Analysis of Colorectal Cancer (CRC) Vesicles Using Multiplex Assays

In this example, plasma samples from patients with colorectal cancer or without CRC (normals) are analyzed according to the general procedure outlined in Example 27. Plasma is prepared according to the protocol of Example 49 and multiplex analysis is performed as in Example 50. Capture antibodies to the vesicle surface antigen proteins in Table 49 were used to screen for biomarkers that detect CRC.

TABLE 49 CRC Capture Antibodies Protein Target Antibody Vendor Catalog Number(s) A33 Anti human glycoprotein A33 Santa Cruz sc50522, sc33014, sc33012 AFP Anti alpha fetal protein antibody Abcam ab8201, ab54745 ALIX Anti Apoptotic linked gene product 2 Thermo Scientific MA1-83977 Interacting Protein X antibody (Pierce) United States A1355-64 Biological ALX4 Anti aristaless-like homeobox 4 antibody ANCA Antineutrophil cytoplasmic antibody APC Anti adenomatous polyposis antibody ASCA Anti saccharomyces cerevisiae antibody Abcam ab19498, ab19731, ab25813 AURKA Anti Aurora A kinase (serine/threonine- Thermo Scientific MA1-34566 protein kinase 6) antibody (Pierce) United States A4190-10D Biological AURKB Anti Aurora B kinase (serine/threonine- Novus Biologicals H00009212-M01A protein kinase 6) antibody Sigma-Aldrich WH0009212M3 B7H3 Anti Cluster of differntiation 276 BioLegend 135602, 135604 antibody BANK1 Anti B-cell scaffold protein with ankyrin Abcam ab93203 repeats 1 antibody BCNP1 Anti B-cell novel protein1 antibody Abcam ab59781 BDNF Anti Brain derived neutrotrophic factor United States B2700-02D antibody Biological CA-19-9 Anti carbohydrate 19-9 antibody United States C0075-03 Biological CCSA-2 Anti colon cancer-specific2 antibody CCSA-3&4 Anti colon cancer-specific3,4 antibody CD10 Anti Cluster of differntiation 10 antibody BD Biosciences 555373 Santa Cruz sc19993 CD24 Anti Cluster of differntiation 24 antibody BD Biosciences bd 555426 Santa Cruz sc19585 CD44 Anti Cluster of differentiation 44 Novus Biologicals, NBP1-04276 antibody LLC Thermo Scientific MA1-19277 (Pierce) CD63 Anti Cluster of differntiation 63 antibody BD Biosciences 556019 CD66 CEA Anti carcinoembryonic antigen (CEA) Santa Cruz family (CD66) antibody CD66e CEA Anti Carcinoembryonic antigen United States C1300-08 (CEA, CD66e) Biological CD81 Anti Cluster of differntiation 81 antibody BD Biosciences 555675 CD9 Anti Cluster of differntiation 9 antibody R&D Systems MAB1880 CDA Anti cytidine deaminase antibody Abcam ab35251 Sigma-Aldrich WH0081602M1 C-Erb2 Anti c-erb2 antibody BD Biosciences 610161 United States C00026-11N1 Biological CRMP-2 Anti collapsin response mediator protein Abcam ab75036 2 antibody AbD Serotec AHP1255 (Raleigh, NC) CRP Anti c-reactive protein antibody Abcam ab13426, ab76434 R&D Systems MAB1707 United States C7907-05A Biological CRTN Anti cortractin antibody CXCL12 Anti Chemokine (C—X—C motif) ligand R&D Systems MAB350 12 antibody Sigma Aldrich WH0006387M1 CYFRA21-1 CYFRA21-1 Medix Biochemica 102221 (Kauniainen, Finland) DcR3 Anti decoy receptor3 antibody DLL4 Anti Delta like protein 4 antibody Abcam ab61031 Cell Signaling 2589 Technology, Inc. DR3 Anti death receptor 3 (apoptosis United States D4012-01B inducing) antibody Biological United States D4012-01B Biological EGFR Anti epidermal growth factor antibody BD Biosciences BD555996 R&D Systems AF231 Epcam Anti Epithelial cellular adhesion R&D Systems MAB 9601 molecule antibody EphA2 Anti Ephrin-A receptor antibody Santa Cruz sc924, sc10746, sc101377, sc135658 FASL Anti human Fas Ligand antibody United States F0019-65B, F0019- Biological 66V FRT Anti Ferritin antibody Santa Cruz sc51887, sc51888 GAL3 Anti Galactose metabolism regulator 3 Santa Cruz sc32790 antibody GDF15 Anti Growth differentiation factor 15 LifeSpan LS-C89472 antibody Biosciences GPCR Anti G-protein coupled receptor antibody GeneTex GTX70591 GPR110 GPR30 Anti GPR30 antibody Abcam ab12563 GRO-1 Anti Human gro alpha antibody Genetex Inc GTX10376 HBD 1 Anti human beta defensin 1 antibody MyBioSource, LLC MBS311954 HBD2 Anti human beta defensin 2 antibody MyBioSource MBS311949 HNP1-3 Anti human neutrophil peptide 1-3 antibody IL-1B Anti Interleukin-1B antibody Sigma-Aldrich Co. WH0003553M1 Thermo Scientific OMA1-03331, (Pierce) MA1-24785 IL8 Anti Interleukin 8 antibody GeneTex, Inc. GTX18649, GTX18672 Thermo Scientific OMA1-03346 (Pierce) United States I8430-06A Biological IMP3 Anti Insulin-like growth factor-II mRNA-binding protein 3 antibody L1CAM Anti L1 cell adhesion molecule antibody GeneTex GTX23200 GenWay Biotech, 20-272-193053 Inc. LAMN Anti Laminin antibody United States L1225-20, L1225- Biological 25, L1225-21 MACC-1 Anti Metastatis associated in colon ProSci Incorporated 5197 cancer-1 antibody MGC20553 Anti FERM domain containing 3 antibody MCP-1 Anti monocyte chemoattractant antibody GeneTex GTX18677, GTX18678 Novus Biologicals NBP1-42360 Thermo Scientific MA1-81750 (Pierce) M-CSF Anti Macrophage colony-stimulating R&D Systems MAB616 factor antibody MIC1 Anti macrophage inhibitory cytokine United States M1199 antibody Biological MIF Anti macrophage migration inhibihitory GeneTex GTX14575 factor antibody Sigma-Aldrich Co. WH0004282M1 Thermo Scientific MA1-20881 (Pierce) MMP7 Anti Matrix metallo Proteinase 7 Novus Biologicals NB300-1000 antibody MMP9 Anti Matrixmetallo Proteinase 9 Novus Biologicals NBP1-28617 antibody MS4A1 Anti membrane spanning 4A1 antibody Sigma-Aldrich WH0000931M1 MUC1 Anti Mucin 1, cell surface associated Santa Cruz sc7313 protein antibody MUC17 Anti Mucin 17, cell surface associated Santa Cruz sc32600, sc32602 protein antibody MUC2 Anti Mucin 2, cell surface associated Santa Cruz sc15334 protein antibody Ncam Anti Ncam R&D Systems MAB2408 NGAL Anti Neutrophil gelatinase-associated Santa Cruz sc18695, sc32600, lipocalin antibody sc50350, sc59622 NNMT Anti nicotinamide N-methyltransferase antibody OPN Anti osteopontin antibody Santa Cruz sc-21742, sc-73631, sc 80262 p53 Anti p53 BioLegend 628202, 645702, 645802 PCSA Anti prostate cell surface antigen Millipore MAB4089, RA1004150 PDGFRB Anti platelet-derived growth factor R&D Systems MAB1263 receptor, beta, subunit antibody Sigma-Aldrich WH0005159M8 PRL Anti prolactin Monoclonal antibody Thermo Scientific MA1-10597 Pierce PSMA Anti prostate specific membrane antigen BioLegend 342502 PSME3 Anti proteasome activator complex Abcam ab91540, ab91542 subunit 3 antibody Reg IV Anti Regenerating islet-derived family, Abcam ab89917 member 4 antibody SCRN1 Anti secrin-1 antibody Sigma-Aldrich HPA024517 Sept-9 Anti septin9 antibody SPARC Anti Aurora A kinase (serine/threonine- R&D Systems MAB941 protein kinase 6) antibody United States O8063-08 Biological SPON2 Anti (Spondin2)Extracelllular matrix protein antibody SPR Anti Seprase (FAB) antibody SRVN Anti tumor necrosis factor-alpha ProMab Mab-2007128 antibody Biotechnologies, Inc. Sigma-Aldrich Co. WH0000332M1 United States S8500-03L Biological TFF3 Anti Trefoil factor 3 (intestinal) antibody Sigma-Aldrich WH0007033M1 TGM2 Anti Transglutaminase-2 antibody Sigma-Aldrich WH0007052M10 TIMP-1 Anti Tissue inhibitor of metallo Sigma-Aldrich WH0007076M1 proteinase-1 antibody TMEM211 Anti Tumor Microenvironment of Santa Cruz sc86534 Metastasis 211 antibody TNF-alpha Anti tumor necrosis factor-alpha R&D Systems MAB610 antibody Thermo Scientific HYB 141-09-02, (Pierce) MA1-21386 TPA Anti tissue polpeptide antibody TPS Anti Tissue polypeptide-specific antibody Trail-R2 Anti TNF-related apoptosis-inducing Thermo Scientific PA1-23497 ligand receptor 2 antibody (Pierce) Trail-R4 Anti TNF-related apoptosis-inducing R&D systems MAB633 ligand receptor 4 antibody TrKB Anti tyrosine Kinase B antibody GeneTex GTX10B41 TROP2 Anti human trophoblast cell-surface Santa Cruz sc80406, sc103908, antibody Biotechnology, Inc. sc103909 Tsg 101 Anti tumor susceptibility gene 101 Santa Cruz sc-101254 antibody TWEAK Anti Tumor necrosis factor like weak United States T9185-01 inducer of apoptosis Biological UNC93A Anti unc93a Santa Cruz sc135539, sc135541 VEGFA Anti Vascular endothelial growth factor United States V2110-16A antibody Biological

As indicated in Table 49, multiple antibodies to a single target biomarker are tested as available and as desired. This allows for assessing vesicle capture using different surface epitopes to determine which provide the desired performance.

FIG. 108A shows the fold-change in vesicles identified using several capture biomarkers that detected vesicle biomarkers overexpressed in CRC as compared to normal. CRC detection using antibody capture of vesicles was performed using 128 total samples consisting of 49 normals, 20 confounders, and 59 CRC. Confounder samples included those having rheumatoid arthritis, asthma, diabetes, bladder cell carcinoma, renal cell carcinoma, and chronic or acute diverticulitis. Of the CRC samples, 16 were Stage I, 19 were Stage II, and 24 were Stage III. As noted, repeated markers in the list are different antibodies to the same antigen. FIG. 108A shows discrimination of Normal and CRC samples using antibodies to multiple biomarkers to capture and detect vesicles. Capture antibodies are shown on the X axis and detection antibodies are shown on the Y axis. Antibodies showing the greatest increase in cancer samples included those to CD66(CEA), A33, EPHA2, TROP2, DR3, UNC93A, NGAL, and MUC17. Table 50 below shows sensitivity and specificity obtained with various capture antibodies:

TABLE 50 CRC Detection using Antibody Capture of Vesicles Marker Sensitivity Specificity DR3 82% 86% STEAP 100% 71% epha2 90% 83% TMEM211 100% 84% unc93A 86% 84% A33 100% 75% CD24 98% 77% NGAL 94% 81% EpCam 90% 62% MUC17 86% 77% TROP2 96% 80% TETS 86% 80%

FIG. 108B shows results from similar experiments except that the Y-axis represents the median fluorescence intensity (MFI) in CRC and normal samples as indicated by the legend. The experiments as shown in FIG. 108B were repeated with a second sample set of 10 CRC samples and 10 normal. Results are shown in FIG. 108C. The markers identified as most overexpressed between cancers and normal were similar using either sample set. FIG. 108D shows the ability to distinguish between normals and CRC using various combinations of the above markers. The plots show MFI on the X and Y axes for the indicated markers. CD24 is used as a colon marker, TROP2 as a cancer marker, and the tetraspanins CD9, CD63 and CD81 are general vesicle markers.

The ability to assay the MFI for multiple surface antigens in a single multiplexed experiment can be used for discovery of optimal target biomarkers. The same techniques can be applied in various settings (e.g., different diseases, different cancers, different target biomarkers, diagnosis, prognosis, theranosis, etc.) to identify novel biomarkers for subsequent assay development.

Example 53 Detection of Colorectal Cancer (CRC) with TMEM211 and CD24

Concentrated microvesicle plasma samples were run on a microsphere platform as described herein. Antibodies to various surface antigens were attached to beads and used to capture microvesicles. Several antibodies showed significant differences between samples derived from CRC and normal patients. The captured microvesicles were labeled with CD9, CD63, and/or CD81. In this example, vesicles from samples are captured using capture antibodies to TMEM211 and/or CD24 (FIGS. 109A-H). Assays are performed according to the methodology outlined in Example 52.

CD24 is a glycosyl phosphatidylinositol-anchored protein (Pierres et al, 1987; Kay et al, 1990; Alternan et al, 1990) expressed on immature cells of most, if not all, major hematopoietic lineages, as well as in developing neurons (Nedelec et al, 1992; Shirasawa et al, 1993; Rougon et al, 1991) and embryonic intestinal, nasal, salivary gland, renal rat epithelial cells (Shirasawa et al, 1993), regenerating muscle (Figarella-Branger et al, 1993). CD24 is usually absent from cells that have reached their final differentiation stage. Expression of CD24 is strongly induced and then repressed again during maturation of T-cells and B-cells (Allman et al, 1992; Bruce et al, 1981; Crispe and Bevan, 1987; Hardy et al, 1991; Husmann et al, 1988; Linton et al, 1989; Symington and Hakamori, 1984; Takei et al, 1981). Erythrocytes are an exception in that they maintain high levels of CD24 expression. CD24 is expressed also in keratinocytes (Magnaldo and Barrandon, 1996), epidermal Langerhans cells (Enk and Katz, 1994), and dendritic cells (Inaba et al, 1992; Ardavin and Shortman, 1992). CD24 is expressed as a major surface antigen on small cell lung carcinomas (Jackson et al. 1992). It is expressed in a variety of carcinomas (Karran et al, 1995; Akashi et al, 1994; Weber et al, 1995).

Nielsen et al (1997) have generated knock-out mice lacking expression of CD24. These mice are characterized by a normal development of T-cells and myeloid cells but show a leaky block in B-cell development with a reduction in late pre-B-cells and immature B-cell populations in the bone marrow. Peripheral B-cell numbers are normal and no impairment of immune function is detected in these mice in a variety of immunization and infection models. Erythrocytes from these mice show a higher tendency to aggregate, are more susceptible to hypotonic lysis in vitro, and have a shorter life-span in vivo.

Lu et al (2000) have reported that a slight overexpression of CD24 in transgenic mice leads to depletion of B-lymphoid cells in the bone marrow, which may be caused by increased cell death by apoptosis of pre-B-cells.

The transmembrane protein 211 (TMEM211) gene encodes a transmembrane protein. The mRNA has four splice variants. TMEM is well conserved amongst various species. Promoter analysis for transcription factor binding sites shows that motifs for CdxA proteins are abundant. Homeobox protein CDX-1 is a protein that in humans is encoded by the CDX1 gene. This gene is a member of the caudal-related homeobox transcription factor gene family. The encoded DNA-binding protein regulates intestine-specific gene expression and enterocyte differentiation. It has been shown to induce expression of the intestinal alkaline phosphatase gene, and inhibit beta-catenin/T-cell factor transcriptional activity.

Colorectal detection using antibody capture of vesicles was performed using 147 total samples consisting of 58 normals, 30 confounders, and 59 CRC (FIG. 109C). Confounder samples included those having conditions as shown in the table in Table 51.

TABLE 51 Confounder samples for vesicle CRC test Confounder Number samples Rheumatoid arthritis 3 Diabetes-Type II, Transitional cell carcinoma of the 2 bladder Rheumatoid arthritis, Marked degenerative arthritis 2 Diabetes, Clear cell renal cell carcinoma. of the kidney 1 Diabetes, Infiltrating ductal carcinoma of the breast 1 Diabetes, Renal cell carcinoma 1 Chronic diverticulosis 9 Lung Cancer 11

ROC analysis for the biomarkers TMEM211 and CD24 are depicted in FIG. 109A and FIG. 109B, respectively. The sensitivity was 92% and the specificity 90% in assays with CD24. Using TMEM211 as the capture antibody and detection antibodies CD9, CD63, CD81, the data in Table 52 was obtained for detection of CRC in the samples described above:

TABLE 52 CRC Detection using TMEM211 True Positive 59 True Negative 58 False Positive 11 False Negative 0 Total 128 Sensitivity Specificity 100.00% 84.06%

In a confirmatory follow-on study, TMEM211 was used to detect colorectal cancer in a cohort of 225 patients comprising 76 CRC samples, 80 normal controls, and 69 confounder samples. Anti-TMEM211 was used as the capture antibody and detection antibodies comprised anti-CD9, anti-CD63, and anti-CD81. The confounder samples are shown in Table 53.

TABLE 53 Confounder samples for vesicle CRC test Confounder Number samples Rheumatoid arthritis 5 Diabetes-Type II, Transitional cell carcinoma of the 3 bladder Diabetes, Clear cell renal cell carcinoma of the kidney 2 Infiltrating ductal carcinoma of the breast 1 Chronic diverticulosis 9 Lung Cancer 49

Results of the follow on study are shown in FIG. 109D-E. FIG. 109D shows the mean fluorescence intensity (MFI) of the samples using TMEM211. Results obtained with the indicated threshold (horizontal bar) are shown in Table 54.

TABLE 54 Results obtained with TMEM211 to detect CRC True Positive 73 True Negative 124 False Positive 25 False Negative 3 Total Samples 225 Sensitivity 96% Specificity 83%

ROC analysis for the same assessed with TMEM211 is depicted in FIG. 109E. The AUC was 0.952.

An additional confirmatory study was performed using a patient cohort consisting of plasma samples from normal controls, Stage I CRC patients, Stage II CRC patients, Stage III CRC patients, and confounders. As described above, the confounder samples were from patients with cancers other than CRC and other disease, including samples from patients with rheumatoid arthritis; diabetes type II and transitional cell carcinoma of the bladder; diabetes and clear cell renal cell carcinoma of the kidney; diabetes and infiltrating ductal carcinoma of the breast; chronic diverticulosis; and lung cancer. Performance of the plasma microvesicle assay for detecting CRC is shown in Table 55.

TABLE 55 Performance of TMEM211 and CD24 to detect CRC TMEM CD24 TMEM & CD24 With confounders True Positive 73 72 72 True Negative 208 205 221 False Positive 69 72 56 False Negative 3 4 4 Total 353 353 353 Sensitivity 96% 95% 95% Specificity 75% 74% 80% Accuracy 80% 78% 83% With confounding cancers and diverticulitis True Positive 73 72 72 True Negative 201 197 212 False Positive 56 60 45 False Negative 3 4 4 Total 333 333 333 Sensitivity 96% 95% 95% Specificity 78% 77% 82% Accuracy 82% 81% 85% Without confounders True Positive 73 72 72 True Negative 135 129 139 False Positive 18 24 14 False Negative 3 4 4 Total 229 229 229 Sensitivity 96% 95% 95% Specificity 88% 84% 91% Accuracy 91% 88% 92%

Results for all samples using TMEM211 and CD24 are shown graphically in FIG. 109F. Using a combination of TMEM211 and CD24, the test identified 13 of 13 Stage I CRC cancers (100% sensitivity), 22 of 23 Stage II CRC cancers (96% sensitivity) and 37 of 40 Stage III CRC cancers (93% sensitivity). Results for TMEM211 and CD24 to distinguish the various classes individually is shown in FIG. 109G and FIG. 109H, respectively.

Example 54 microRNA Overexpression in Colorectal Cancer Cell Lines

TaqMan Low Density Array (TLDA) miRNA cards were used to compare expression of miRNA in CRC cell lines versus normal vesicles. The miRNA was collected and analyzed using the TaqMan® MicroRNA Assays and Arrays systems from Applied Biosystems, Foster City, Calif. Applied Biosystems TaqMan® Human MicroRNA Arrays were used according to the Megaplex™ Pools Quick Reference Card protocol supplied by the manufacturer. See Example 17.

FIG. 110 illustrates TLDA miRNA card comparison of colorectal cancer (CRC) cell lines versus normal vesicles. The cell lines include LOVO, HT29, SW260, COLO205, HCT116 and RKO. The plot shows a 2-3 fold increase in expression in the CRC cell lines compared to normal controls. These miRNAs were not overexpressed in melanoma cells.

The sequences assayed in FIG. 110 include miR-548c-5p, miR-362-3p, miR-422a, miR-597, miR-429, miR-200a, and miR-200b.

Example 55 microRNA to Detect CRC

MicroRNAs (miRs) were obtained from vesicles isolated from 12 CRC patients and 4 control patients. The samples were analyzed for two miRs, miR 92 and miR 491, that had been identified as overexpressed in CRC cell lines. FIG. 111A shows that higher levels of these miRs were also found in CRC patient samples versus normals. FIG. 111B shows that together miR 92 and miR 21 result in improved differentiation of normal and CRC samples. FIG. 111C shows the addition of additional miRs that can be multiplexed with miR 92 and miR 21. The figure shows multiplexing of miR 92, miR 21, miR 9 and miR 491 to detect CRC.

Example 56 KRAS Sequencing in CRC Cell Lines and Patient Samples

KRAS RNA was isolated from vesicles derived from CRC cell lines and sequenced. RNA was converted to cDNA prior to sequencing. Sequencing was performed on the cell lines listed in Table 56:

TABLE 56 CRC cell lines and KRAS sequence DNA or KRAS KRAS Vesicle Genotype Genotype Cell Line cDNA Exon 2 Exon 3 Colo 205 Vesicle cDNA WT WT Colo 205 DNA WT WT HCT 116 Vesicle cDNA c.13G > GA WT HCT 116 DNA c.13G > GA WT HT29 Vesicle cDNA WT WT Lovo Vesicle cDNA c.13G > GA WT Lovo DNA c.13G > GA WT RKO Vesicle cDNA WT WT SW 620 Vesicle cDNA c.12G > T WT

Table 56 and FIG. 112 show that the mutations detected in the genomic DNA from the cell lines was also detected in RNA contained within vesicles derived from the cell lines. FIG. 112 shows the sequence in HCT 116 cells of cDNA derived from vesicle mRNA in (FIG. 112A) and genomic DNA (FIG. 112B).

Twelve CRC patient samples were sequenced for KRAS. As shown in Table 57, all were wild type (WT). All patient samples received a DNase treatment during RNA Extraction. RNA was extracted from isolated vesicles. All 12 patients amplified for GAPDH demonstrating RNA was present in their vesicles.

TABLE 57 CRC patient samples and KRAS sequence KRAS Genotype KRAS Genotype Sample Sample Type Stage Exon 2 Exon 3 61473a6 Colon Ca 1 WT WT 62454a4 Colon Ca 1 WT WT 110681a4 Colon Ca 1 WT Failed sequencing 28836a7 Colon Ca 1 WT Failed sequencing 62025a2 Colon Ca 2a WT WT 62015a4 Colon Ca 2a WT WT 110638a3 Colon Ca 2a WT WT 110775a3 Colon Ca 2a WT WT 35512a5 Colon Ca 3 WT WT 73231al Colon Ca 2a WT WT 85823a3 Colon Ca 3b WT WT 23440a7 Colon Ca 3c WT WT 145151A2/3 Normal WT WT 139231A3 Normal WT Failed sequencing 145155A4 Normal WT Failed sequencing 145154A4 Normal WT Failed sequencing

In a patient sample wherein the patient was found positive for the KRAS 13G>A mutation, the KRAS mutation from the tumor of CRC patient samples could also be identified in plasma-derived vesicles from the same patient. FIG. 112 shows the sequence in this patient of cDNA derived from vesicle mRNA in plasma (FIG. 112C) and also genomic DNA derived from a fresh frozen paraffin embedded (FFPE) tumor sample (FIG. 112D).

Example 57 CRC miRs in Vesicle Fractions

In this example, miRNAs found in vesicles of size 50-100 nm (small vesicles) and size 100-1,000 nm (large vesicles) fractions in serum was compared.

RNA from three 1 ml colorectal cancer (CRC) patient serum samples was isolated using the Exomir kit from Bioo Scientific (Austin, Tex.). This method uses a filter to separate the large vesicle and small vesicle portions. The serum was spun in a centrifuge before isolation to remove cellular debris.

40 ng of RNA was added to an RT-PCR reaction and the Exiqon miRCURY LNA™ Universal RT microRNA PCR Human Panels I and II (Exiqon, Inc, Woburn, Mass.) were used to evaluate the relative expression of 742 miRs. The results were normalized and analyzed using GeneSpring GX 11.0 (Agilent Technologies, Inc., Santa Clara, Calif.) following manufacturer's protocol. The measurements for each sample were normalized to inter-plate calibrators and RT-PCR calibrators, a paired t-test was used to compare the large vesicle—small vesicle paired samples. Statistical significance was determined at an uncorrected p value of 0.05. Five miRs were found to be significantly differentially expressed. See Table 58.

TABLE 58 miR levels in Large and Small Vesicle Populations Isolated from Paired Samples Regulation in Large miR Detector p-value versus Small Vesicles Fold Change hsa-miR-376c 0.0067 up 56.4 hsa-miR-652 0.0015 up 287.8 hsa-miR-221* 0.049 down 42.1 hsa-miR-215 0.019 down 46.0 hsa-miR-324-5p 0.028 up 36.2

A fold change comparison identified 361 miRs that had a greater than two-fold difference between large vesicles and small vesicles. Eight miRs were detected in large vesicles but not small vesicles and three miRs were only detected in small vesicles but not large vesicles. See Table 59.

TABLE 59 miR levels in Large Vesicle and Small Vesicle Populations Isolated from Paired Samples Detected Only in Large Vesicles Detected Only in Small Vesicles hsa-miR-376c hsa-miR-215 hsa-miR-652 hsa-miR-582-5p hsa-miR-324-5p hsa-miR-1296 hsa-miR-28-5p hsa-miR-190 hsa-miR-590-5p hsa-miR-202 hsa-miR-195

These data reveal that the miRNA content of large vesicles and small vesicles in a sample is similar but not necessarily identical. Such differences can be used to optimize diagnostic tests.

Example 58 CRC Marker Combinations

In this Example, assays were performed as described in Example 52 above. The sample set comprised 462 samples, 256 were from individuals with biopsy confirmed CRC and 206 samples were normals (defined for these purposes as individuals without CRC). The 256 cancer samples included 12 unstaged samples, 57 stage I, 103 stage II, 78 stage III, and six stage IV. Normal samples are age-range matched self declared disease free individuals. Antibodies to the indicated vesicle surface antigens MUC1, GPCR 110, TMEM211 and CD24 were attached to beads and used to capture microvesicles in the plasma samples. The bead-captured microvesicles were labeled with PE-labeled CD9, CD63, CD81 and fluorescence of bound vesicles was determined. Fluorescent intensities for the markers were used to classify the samples as CRC versus normal.

In the Human Gene Ontology (HUGO) database, GPCR 110 is also referred to under the approved name G protein-coupled receptor 110 or the approved symbol GPR110. See www.genenames.org/data/hgnc_data.php?hgnc_id=18990. Two alternative transcripts are identified as REFSEQ proteins NP_(—)079324.2 and NP_(—)722582.2. The GRP110 gene encodes a cell membrane protein.

The HUGO approved name for MUC1 is mucin 1, cell surface associated. See www.genenames.org/data/hgnc_data.php?hgnc_id=7508. Seven alternative transcripts are identified as REFSEQ proteins NP_(—)001018016.1, NP_(—)001018017.1, NP_(—)001037855.1, NP_(—)001037856.1, NP_(—)001037857.1, NP_(—)001037858.1, and NP_(—)002447.4. The MUC1 gene is a member of the mucin family and encodes a membrane bound, glycosylated phosphoprotein that plays a role in cell adhesion.

MUC1, GPCR 110, TMEM211 and CD24 were used as capture antibodies for detecting the microvesicles in the CRC and normal plasma. The captured vesicles were labeled as described with CD9, CD63, and CD81. Median fluorescence intensity (MFI) cut off thresholds were determined to optimally separate cancer patients from normals. If a sample was elevated in a marker, then the sample was considered positive for colorectal cancer (CRC). Diagnostic performance of each individual marker is shown in Table 60.

TABLE 60 MFI of microvesicles in plasma samples for CRC versus normal for individual markers. Muc1 GPCR 110 TMEM211 CD24 True Positive 232 225 235 212 True Negative 162 168 151 182 False Positive  44  38  55  26 False Negative  24  31  21  46 Total 462 Sensitivity 90.63% 87.89% 91.80% 82.17% Specificity 78.64% 81.55% 73.30% 87.50% Accuracy 85.28% 85.06% 83.55% 84.55% MFI Cutoff 390 360 200 600

Detection of CRC using various marker combinations is shown in Tables 61-63. In Table 63, a sample was considered positive for colorectal cancer (CRC) if either of the markers within the logical disjunction (i.e., “or”) was positive. As an example, “Muc1 & GPCR & (TMEM or CD24)” is considered positive if Muc1 is positive and GCPR (i.e., GPR110) is positive, and either of TMEM (i.e., TMEM211) or CD24 is positive. By comparing the results of Table 60 with those of Tables 61-63, it is observed that single markers can provide highly accurate separation of CRC and normal samples, but that detection of CRC can be improved in some cases using multiple markers.

TABLE 61 MFI of microvesicles in plasma samples for CRC versus normal for two marker combinations Muc1 Muc1 Muc1 GPCR GPCR TMEM GPCR TMEM CD24 TMEM CD24 CD24 True Positive 223 232 211 224 210 212 True Negative 171 171 181 174 182 183 False Positive 35 35 25 32 24 25 False Negative 33 24 45 32 46 46 Total 462 462 462 462 462 466 Sensitivity 87.11% 90.63% 82.42% 87.50% 82.03% 82.17% Specificity 83.01% 83.01% 87.86% 84.47% 88.35% 87.98% Accuracy 85.28% 87.23% 84.85% 86.15% 84.85% 84.76%

TABLE 62 MFI of microvesicles in plasma samples for CRC versus normal for multiple marker combinations Muc1 Muc1 Muc1 GPCR GPCR GPCR TMEM TMEM TMEM CD24 CD24 CD24 All 4 True 223 211 211 211 211 Positive True 174 182 182 183 183 Negative False  32  24  24  25  25 Positive False  33  45  45  47  47 Negative Total 462 462 462 466 466 Sensitivity 87.11% 82.42% 82.42% 81.78% 81.78% Specificity 84.47% 88.35% 88.35% 87.98% 87.98% Accuracy 85.93% 85.06% 85.06% 84.55% 84.55%

TABLE 63 MFI of microvesicles in plasma samples for CRC versus normal for multiple marker combinations Muc1 & TMEM & GPCR & Muc1 & GPCR & (Muc1 or GPCR & Muc1 & TMEM & (GPCR or (Muc1 or GPCR) & (TMEM or (GPCR or (Muc1 or TMEM) & TMEM) & TMEM & CD24) CD24) CD24) CD24 CD24 CD24 True Positive 223 224 223 211 210 212 True Negative 174 174 174 182 182 183 False Positive 32 32 32 24 24 25 False Negative 33 32 33 45 46 46 Total 462 462 462 462 462 466 Sensitivity 87.11% 87.50% 87.11% 82.42% 82.03% 82.17% Specificity 84.47% 84.47% 84.47% 88.35% 88.35% 87.98% Accuracy 85.93% 86.15% 85.93% 85.06% 84.85% 84.76%

FIG. 113 shows a plot in which TMEM211 and MUC1 were used as capture antibodies for detecting the microvesicles in the CRC and normal plasma. The captured vesicles were labeled as described with CD9, CD63, and CD81. Median fluorescence intensity (MFI) cut off thresholds were determined to optimally separate cancer patients from normals. If a sample was elevated in both markers, then the sample was considered positive for colorectal cancer (CRC). Diagnostic performance of each individual marker and the combination of TMEM211 and MUC1 is shown in Tables 60 and 61 above.

Example 59 Vesicle Biosignatures for Breast Cancer (BCa)

Antibodies to a number of antigens of interest were tethered to beads and used to capture vesicles in blood samples from 10 subjects with breast cancer or 10 normals (i.e., no breast cancer) following the methodology of Examples 49-50. Capture antibodies were directed to the vesicle antigens described in this Example. The bead captured vesicles were detected with fluorescently labeled antibodies against the tetraspanins CD9, CD63 and CD81. The median fluorescence intensity (MFI) of the captured and labeled vesicles was measured using laser detection.

The analysis was perfomed using a panel of capture antibodies. There were significant differences in the MFI of detected vesicles between breast cancer and normal plasma when analysis was performed using the following capture antibodies to the following vesicle antigens: CD9, HSP70, Gal3, MIS, EGFR, ER, ICB3, CD63, B7H4, MUC1, DLL4, CD81, ERB3, VEGF, BCA225, BRCA, CA125, CD174, CD24, ERB2, NGAL, GPR30, CYFRA21, CD31, cMET, MUC2 and ERB4.

Follow on experiments were performed using 10 breast cancer samples and 10 normal samples and additional capture antibodies. FIG. 114A illustrates a graph depicting the fold change over normal of the indicated biomarkers expressed in a breast cancer. The markers include from left to right CD9, EphA2, EGFR, B7H3, PSMA, PCSA, CD63, STEAP, STEAP, CD81, B7H3, STEAP1, ICAM1 (CD54), PSMA, A33, DR3, CD66e, MFG-8e, EphA2, Hepsin, TMEM211, EphA2, TROP-2, EGFR, Mammoglobin, Hepsin, NPGP/NPFF2, PSCA, 5T4, NGAL, NK-2, EpCam, NGAL, NK-1R, PSMA, 5T4, PAI-1, and CD45. Multiple bars for the same antigen indicate the use of different capture antibodies that may recognize different epitopes.

FIG. 114B illustrates the level of various biomarkers detected in vesicles derived from breast cancer cell lines MCF7, T47D and MDA. T47D and MDA are metastatic cell lines. Antigens observed in breast cancer cell lines include CD9, MIS Rii, ER, CD63, MUC1, HER3, STAT3, VEGFA, BCA, CA125, CD24, EPCAM, and ERB B4.

The ability to assay multiple vesicle biomarkers in a single multiplexed experiment can be used to create a biosignature for breast cancer and for discovery of optimal target biomarkers for additional biosignatures. The same techniques can be applied in various settings (e.g., different diseases, different cancers, different target biomarkers, diagnosis, prognosis, theranosis, etc.) to identify novel biomarkers for subsequent assay development.

Example 60 Analysis of Breast Cancer (BCa) Vesicles Using Multiplex Assays

In this example, plasma samples from patients with breast cancer (BCa) or without BCa (normals) are analyzed according to the general procedure outlined in Example 27. Plasma is prepared according to the protocol of Example 49 and multiplex analysis is performed as in Example 50. Capture antibodies to the vesicle surface antigen proteins in Table 64 were used to screen for biomarkers that detect BCa.

TABLE 64 BCa Capture Antibodies Antibody Target Antibody Vendor Catalog Number PGP9.5 clone 3D9 Genway 20-002-35062 CD9 209306 R&D MAB1880 HSP70 W27 Biolegend 648002 gal3-b2c10 B2C10 Santa Cruz sc-32790 EGFR EGFR.1 BD Biosciences 555996 iC3b 013III-1.16 Thermo MA1-82814 PSMA LNI-17 Biolegend 342502B PCSA 5E10 MILLIPORE MAB4089 CD63 H5C6 BD 556019 MUC1 c.Vu4H5 VU4H5 Santa Cruz sc-7313 DLL4 Polyclonal Santa Cruz sc-18639 CD81 JS-81 BD 555675 B7-H3 MIH35 BioLegend 135604 HER 3 (ErbB3) Polyclonal US Biological E3451-36A MART-1 0.N.396 US Biological M2410 PSA 181811 R&D MAB13442 VEGF A 5J63 US Biological V2110-05D TIMP-1, 4d12 4D12 Sigma-Aldrich WH0007076M1 GPCR GPR110 Polyclonal GeneTex GTX70591 EphA2 (H-77) Polyclonal Santa Cruz sc-10746 MMP9 c.SB15C SB15c Novus NBP1-28617 Biologicals mmp7 Polyclonal Novus NB300-1000 TMEM211 c.c15 Polyclonal Santa Cruz sc-86534 UNC93a c.c13 Polyclonal Santa Cruz sc135539 BRCA 1.A.26 US Biological B2708-06 CA125 (MUC16) 8J453 US Biological C0050-01D Mammaglobin Polyclonal Santa Cruz sc-48328 CD174 (Lewis y) 8.S.289 US Biological L2056 CD66e CEA (poly) Polyclonal US biological C1300-08 CD24 c.sn3 SN3 Santa Cruz sc-19585 C-erbB2 42/c-erbB2-2 BD 610161 CD10 HI10a BD 555373 NGAL c.h130 Polyclonal Santa Cruz sc-50350 epcam 158206 R&D MAB9601 CEA 8J503 US Biological C1300-01B (carcinoembryonic Antigen) GPR30 Polyclonal GeneTex GTX100001 CYFRA21-1 1603 MedixMab 102221 OPN c.lfmb-14 LFMb-14 Santa Cruz sc-73631 ASPH (D01P) Polyclonal Novus H00000444-D01P ASPH (D03) Polyclonal Novus H00000444-D03 ASPH (G-20) Polyclonal Santa Cruz sc-33367 MUC17 c.c19 Polyclonal Santa Cruz sc-32602 hVEGFR2 89106 R&D MAB3572 p53 c.do-1 DO-1 BioLegend 645702 ASPH (H-300) Polyclonal Santa Cruz sc-66939 MUC2 c.h-300 Polyclonal Santa Cruz sc-15334 NCAM 301040 R&D MAB2408 ASPH (A-10) A-10 Santa Cruz sc-271391 ASPH (AB2) Polyclonal Sigma-Aldrich AV51357 ASPH Polyclonal Sigma-Aldrich AV51356 ASPH (666-680) Polyclonal Sigma-Aldrich A7110 ErbB4 Polyclonal US Biological E3451-40F ASPH (246-260) Polyclonal Sigma-Aldrich A6985 SPB 2Q2279 US Biological S8401-02 SPC Polyclonal US Biological U2575-03 CD9 209306 R&D MAB1880 MS4A1 5c11 5C11 Sigma WH0000931M1 EphA2 c.20 Polyclonal Santa Cruz sc-924 MIS RII R&D AF4749 HER2 (ErbB2) 191924 R&D MAB1129 ER 5G106 US Biological E3564-89 PR (B) H5344 R&D PP-H5344-00 MRP8 8L802 US Biological M4688-36A CD63 H5C6 BD 556019 B7H4 Polyclonal US Biological B0000-35A TGM2 c.2F4 2F4 Sigma-Aldrich WH0007052M10 CD81 JS-81 BD 555675 DR3 Polyclonal US Bio D4012-01B STAT 3 Polyclonal US Biological S7971-01M MACC-1 (poly) Polyclonal ProSci Inc 5197 TrKB (poly) Polyclonal Novus NB100-92063 biologicals IL 6 Unc 8H12 Invitrogen AHC0762 OPG - 13 OPG-13 Biovendor RD182003110-13 IL6R 2G6 Sigma WH0003570M1 EZH2 2C3 ABD serotec MCA4898Z SCRN1 Polyclonal Sigma HPA024517 TWEAK Polyclonal US biological T9185-01 SERPINB3 2F5 Sigma WH0006317M1 CDAC1 1a2 1A2 Sigma WH0081602M1 BCA-225 BRST-1 US Biological B0395-10B DR3 Polyclonal US Biological D4012-01B a33 n15 sc33012 Polyclonal Santa Cruz sc-33012 NPGP/NPFF2 Polyclonal Santa Cruz sc-46206 TIMP1 poly us bio Polyclonal US Biological T5580-08B BDNF us bio Polyclonal US Bio B2700-02D FRT c.f23 F23 Santa Cruz sc-51887 Ferritin heavy chain F31 Santa Cruz sc-51888 seprase R&D 427819 R&D MAB3715 p53_Clone do-7 DO-7 BioLegend 645802 ost akm2a1 AKm2A1 Santa Cruz sc-21742 LDH Polyclonal US Bio L1011-12H HSP 8L126 US Bio H1830-94G ost zz09 ZZ09 Santa Cruz sc-80262 p53_Clone BP53- BP53-12 BioLegend 628202 12 CXCL12 79014 R&D MAB310 HAP 1.C.1 USBIO H1820-05 CRP 3H109 US Bio C7907-05A Gro-alpha Polyclonal GeneTex GTX10376 Tsg 101 c.Y16J Y16J Santa Cruz sc101254 GDF15 LIFESPAN LS-C89472 BIOSC. YB-1

As indicated in Table 64, multiple antibodies to a single target biomarker are tested as available and as desired. This allows for assessing vesicle capture using different surface epitopes to determine which provide the desired performance for detecting BCa.

Example 61 Vesicle Biosignatures for Lung Cancer (LCa)

Antibodies to a number of antigens of interest were tethered to beads and used to capture vesicles in plasma samples from subjects with lung cancer, normal controls (i.e., no lung cancer), or other diseases using methodology as outlined in Examples 49-50. Capture antibodies were directed to the vesicle antigens described in this Example. The bead captured vesicles were detected with fluorescently labeled antibodies against the tetraspanins CD9, CD63 and CD81. The median fluorescence intensity (MFI) of the captured and labeled vesicles was measured using laser detection.

In a first set of experiments, vesicles were detected following the methodology above in plasma samples from 10 normal control samples, 10 non-lung cancer samples, and 10 lung cancer samples as shown in Table 65.

TABLE 65 Samples Diagnosis Stage Normal Control -male NA Normal Control -male NA Normal Control -male NA Normal Control -male NA Normal Control -male NA Normal Control - Female NA Normal Control - Female NA Normal Control - Female NA Normal Control - Female NA Normal Control - Female NA Endometrioid adenocarcinoma Unstaged Endometrioid adenocarcinoma Unstaged Infiltrating ductal carcinoma of the breast Unstaged Metastatic carcinoma of lymph node(from breast) Unstaged Metastatic carcinoma of lymph node(from breast) Unstaged Renal cell carcinoma of the kidney Unstaged Renal cell carcinoma of the kidney Unstaged Renal cell carcinoma of the kidney Unstaged Sclerosing stromal tumor of the ovary Unstaged Serous papillary cystadenoma of the ovary Unstaged Bronchioloalveolar carcinoma of the lung IA Large cell carcinoma of the lung IIA Squamous cell carcinoma of the lung IIA Squamous cell carcinoma of the lung IA Squamous cell carcinoma of the lung IIA Tubular adenocarcinoma of the lung IB Large cell carcinoma of the lung IIB Adenocarcinoma of the lung IIB Adenocarcinoma of the lung IIIA Adenocarcinoma of the lung IB

Vesicles in the samples were captured using antibodies to the antigens listed in FIG. 115A and FIG. 115B using capture antibodies bound to beads. From left to right, the antigens in the figures are: SPB, SPC, TFF3, PGP9.5, CD9, MS4A1, NDUFB7, Cal3, iC3b, CD63, MUC1, TGM2, CD81, B7H3, DR3, MACC1, TrkB, TIMP1, GPCR (GPR110), MMP9, MMP7, TMEM211, TWEAK, CDADC1, UNC93, APC, A33, CD66e, TIMP1, CD24, ErbB2, CD10, BDNF, Ferritin, Ferritin, Seprase, NGAL, EpCam, ErbB2, Osteopontin (OPN), LDH, OPN, HSP70, OPN, OPN, OPN, OPN, MUC2, NCAM, CXCL12, Haptoglobin (HAP), CRP, and Gro-alpha. Different capture antibodies are used where the same antigen appears multiple times, e.g., Erbb2, Ferritin and Osteopontin. The different antibodies may recognize different epitopes of the same biomarker.

The captured vesicles were detected using fluorescently labeled antibodies to CD9, CD63 and CD81. The detected median fluorescence levels (MFI) are shown on the Y-axis in FIG. 115B. Ratios of the fluorescence in normals versus lung cancer samples, or normals versus non-lung cancer samples, are shown in FIG. 115A. FIG. 115C shows the MFI of EPHA2 (i), CD24 (ii), EGFR (iii), and CEA (iv) in samples from lung cancer patients and normal controls.

Concentrated microvesicle plasma samples from lung cancer and normal patients were collected and analyzed as in Examples 49-50. 69 patients were screened for 31 capture antibodies to vesicle surface antigens. FIG. 115D presents a graph of mean fluorescence intensity (MFI) on the Y axis for lung cancer and normal samples, with capture antibodies indicated along the X axis. The captured vesicles were detected using fluorescently labeled antibodies to CD9, CD63 and CD81. From left to right, the antigens in the figures are: SPB, SPC, NSE, PGP9.5, CD9, P2RX7, NDUFB7, NSE, Gal3, Osteopontin, CHI3L1, EGFR, B7H3, iC3b, MUC1, Mesothelin, SPA, TPA, PCSA, CD63, AQP5, DLL4, CD81, DR3, PSMA, GPCR 110 (GPR110), EPHA2, CEACAM, PTP, CABYR, TMEM211, ADAM28, UNC93a, A33, CD24, CD10, NGAL, EpCam, MUC17, TROP2 and MUC2. Antigens most able to distinguish between lung cancer and normal samples include SPB, SPC, PSP9.5, NDUFB7, Gal3, iC3b, MUC1, GPCR 110, CABYR and MUC17.

In another set of related experiments, the levels of a separate but overlapping panel of vesicle surface biomarkers was assessed in 115 lung and 78 normals samples. Of the lung cancer samples, there were 35 Stage I, 53 Stage II, and 27 Stage III lung cancers. As above, vesicles were captured in plasma samples from the cohort using capture antibodies to the indicated surface antigens, and the captured vesicles were detected with labeled detection antibodies to CD9, CD63 and CD81. Results are shown in Table 66 and FIG. 115E. From left to right, the antigens in the FIG. 115E are: CD9, CD63, CD81, B7H3, PRO GRP, CYTO 18, FTH1, TGM2, CENPH, ANNEXIN I, ANNEXIN V, ERB2, EGFR, CRP, VEGF, CYTO 19, CCL2, Osteopontin (OST19), Osteopontin (OST22), BTUB, CD45, TIMP, NACC1, MMP9, BRCA1, P27, NSE, M2PK, HCG, MUC1, CEA, CEACAM, CYTO 7, EPCAM, MS4A1, MUC1, MUC2, PGP9, SPA, SPA, SPD, P53, GPCR (GPR110), SFTPC, UNCR2, NSE, INGA3, INTG b4, MMP1, PNT, RACK1, NAP2, HLA, BMP2, PTH1R, PAN ADH, NCAM, CD151, CKS1, FSHR, HIF, KRAS, LAMP2, SNAIL, TRIM29, TSPAN1, TWIST1, ASPH and AURKB. Table 66 ranks the markers by accuracy for differentiating between cancer and non-cancer samples. As indicated in the table, not all markers were run on all samples due to sample quantity and the like.

TABLE 66 Marker Panel Results for Lung Cancer Sample True True False False Marker Size Sensitivity Specificity Accuracy Positive Negative Positive Negative NSE 117 57.38 96.43 76.07 35 54 2 26 TRIM29 46 58.62 100 73.91 17 17 0 12 CD63 76 74.07 68.18 72.37 40 15 7 14 CD151 47 70 76.47 72.34 21 13 4 9 ASPH 47 60 94.12 72.34 18 16 1 12 LAMP2 47 56.67 100 72.34 17 17 0 13 TSPAN1 47 56.67 100 72.34 17 17 0 13 SNAIL 46 58.62 94.12 71.74 17 16 1 12 CD45 164 50.55 95.89 70.73 46 70 3 45 CKS1 47 53.33 100 70.21 16 17 0 14 NSE 77 43.9 100 70.13 18 36 0 23 FSHR 46 51.72 100 69.57 15 17 0 14 OPN 193 54.78 91.03 69.43 63 71 7 52 FTH1 193 52.17 94.87 69.43 60 74 4 55 PGP9 193 51.3 96.15 69.43 59 75 3 56 ANNEXIN 1 193 50.43 97.44 69.43 58 76 2 57 SPD 193 48.7 98.72 68.91 56 77 1 59 CD81 112 49.18 92.16 68.75 30 47 4 31 EPCAM 188 52.17 94.52 68.62 60 69 4 55 PTH1R 95 55.56 93.75 68.42 35 30 2 28 CEA 193 47.83 98.72 68.39 55 77 1 60 CYTO 7 193 47.83 98.72 68.39 55 77 1 60 CCL2 164 42.86 100 68.29 39 73 0 52 SPA 192 47.37 98.72 68.23 54 77 1 60 KRAS 47 50 100 68.09 15 17 0 15 TWIST1 47 50 100 68.09 15 17 0 15 AURKB 47 50 100 68.09 15 17 0 15 MMP9 191 47.79 97.44 68.06 54 76 2 59 P27 169 56.31 86.36 68.05 58 57 9 45 MMP1 172 49.04 97.06 68.02 51 66 2 53 HLA 96 53.12 96.88 67.71 34 31 1 30 HIF 46 48.28 100 67.39 14 17 0 15 CEACAM 193 51.3 91.03 67.36 59 71 7 56 CENPH 193 46.09 98.72 67.36 53 77 1 62 BTUB 193 46.09 98.72 67.36 53 77 1 62 INTG b4 172 45.19 100 66.86 47 68 0 57 EGFR 193 46.09 97.44 66.84 53 76 2 62 NACC1 193 45.22 98.72 66.84 52 77 1 63 CYTO 18 193 44.35 100 66.84 51 78 0 64 NAP2 96 50 100 66.67 32 32 0 32 CYTO 19 192 45.61 97.44 66.67 52 76 2 62 ANNEXIN V 192 44.74 97.44 66.15 51 76 2 63 TGM2 193 45.22 96.15 65.8 52 75 3 63 ERB2 193 43.48 98.72 65.8 50 77 1 65 BRCA1 193 43.48 98.72 65.8 50 77 1 65 B7H3 146 41.18 100 65.75 35 61 0 50 SFTPC 172 43.27 100 65.7 45 68 0 59 PNT 172 43.27 100 65.7 45 68 0 59 NCAM 96 48.44 100 65.62 31 32 0 33 MS4A1 192 42.98 98.72 65.62 49 77 1 65 P53 173 42.86 100 65.32 45 68 0 60 INGA3 173 42.86 100 65.32 45 68 0 60 MUC2 193 46.09 93.59 65.28 53 73 5 62 SPA 193 43.48 97.44 65.28 50 76 2 65 OPN 193 42.61 98.72 65.28 49 77 1 66 CD63 112 45.9 88.24 65.18 28 45 6 33 CD9 112 36.07 100 65.18 22 51 0 39 MUC1 192 41.23 100 65.1 47 78 0 67 UNCR3 173 42.86 98.53 64.74 45 67 1 60 PAN ADH 96 48.44 96.88 64.58 31 31 1 33 HCG 96 46.88 100 64.58 30 32 0 34 TIMP 193 41.74 97.44 64.25 48 76 2 67 PSMA 103 41.27 100 64.08 26 40 0 37 GPCR 173 40 100 63.58 42 68 0 63 RACK1 96 45.31 100 63.54 29 32 0 35 PCSA 167 40.59 98.48 63.47 41 65 1 60 VEGF 193 37.39 100 62.69 43 78 0 72 BMP2 96 45.31 96.88 62.5 29 31 1 35 CD81 76 50 90.91 61.84 27 20 2 27 CRP 193 38.26 94.87 61.14 44 74 4 71 PRO GRP 193 33.04 98.72 59.59 38 77 1 77 B7H3 76 44.44 95.45 59.21 24 21 1 30 MUC1 92 33.33 100 56.52 20 32 0 40 M2PK 188 27.93 94.81 55.32 31 73 4 80 CD9 76 38.89 90.91 53.95 21 20 2 33 PCSA 29 62.5 0 51.72 15 0 5 9 PSMA 76 24.07 95.45 44.74 13 21 1 41

The data for the markers as shown in Table 66 and FIG. 121E were further used to build multiple marker panels to improve test performance, i.e., sensitivity, specificity and accuracy. Table 67 and FIG. 121F show the results obtained using a panel of PRO GRP, MMP9 and CENPH. FIG. 121F is a 3-dimensional plot show differentiation of the cancer (open squares) and non-cancer samples (closed triangles). As shown in Table 67, the combination of markers was able to improve sensitivity as compared to any individual marker while maintaining a high specificity (85%).

TABLE 67 Triple Marker Combination Panel Results for Lung Cancer PRO GRP MMP9 CENPH All Three True Positive 28 44 37 37 True Negative 48 40 45 35 False Positive 3 11 6 6 False Negative 33 17 24 8 Sensitivity 46% 72% 61% 82% Specificity 94% 78% 88% 85%

The ability to assay multiple vesicle biomarkers in a single multiplexed experiment can be used to create a biosignature for lung cancer and for discovery of optimal target biomarkers for additional biosignatures. The same techniques can be applied in various settings (e.g., different diseases, different cancers, different target biomarkers, diagnosis, prognosis, theranosis, etc.) to identify novel biomarkers for subsequent assay development.

Example 62 Biosignature on Circulating Microvesicles as Tool for Detecting Lung Cancer

Circulating microvesicles (cMV) are cell derived, membrane-bound structures that are abundantly present in the blood. Tumor cells produce large quantities of cMV, and their production has been shown to correlate with tumor invasiveness and resistance to therapy. In this example, the protein composition of cMV in patients with non-small cell lung cancer (NSCLC) was analyzed. Using a decision tree, a biosignature that can predict tumor presence was developed.

cMV that was isolated from blood derived from patients with NSCLC was compared to cMV in similar samples from control patients to ascertain the presence of biomarkers indicative of cancer. Antibodies coupled to fluorescently labeled beads were used to detect the presence of cMV in the samples, using methodology as described herein.

Using a multiplexing analysis and a decision tree, we have optimized the threshold signal to an optimal level able to segregate the two populations. From an initial cohort of 111 patients with NSCLC and controls, using a panel of 63 specific biomarkers, we developed an assay with high specificity and sensitivity. This assay is based on an algorithm derived from a decision three that includes use of four capture antibodies to the following vesicle biomarkers: one general cMV marker, CD81, and three markers for lung cancer, Surfactant Protein D (SPD), Surfactant Protein A (SP-A) and Osteopontin (OPN). Using the capture antibodies coupled on beads and detection using anti-tetraspanin detection antibodies and laser based detectors as described herein, fluorescence intensities stemming from cMV bound to labeled beads were measured in blood samples from a cohort of 40 patients affected by early stage of lung cancer and 25 controls without lung cancer. The resulting mean fluorescence intensity (MFI) values were run through an algorithm to discriminate cancer. A tree view of the algorithm is presented in FIG. 116. The numbers between each marker indicate the MFI threshold for that step, which can be adjusted to favor sensitivity or specificity as described. An MFI above 917 for SPD is indicated as positive for cancer. If the MFI for SPD is ≦917, the MFI for CD81 is next considered. An MFI less than 627 for CD81 is indicated as positive for cancer. If the MFI for CD81 is ≧627, the MFI for SP-A is next considered. An MFI less than 375 for SP-A is indicated as negative for cancer. If the MFI for SP-A is ≧375, the MFI for OPN is next considered. An MFI less than 80 for OPN is indicated as positive for cancer. An MFI ≧80 for OPN is indicated as negative for cancer.

Results of the analysis using the decision tree of FIG. 116 are shown in Table 68. As shown in the table, the analysis identified lung cancer positive samples with a sensitivity of 93%, specificity of 92% and accuracy of 92%.

TABLE 68 Circulating Microvesicle Detection of Lung Cancer Description Patients True Positive 37 True Negative 23 False Positives 2 False Negatives 3 Total 65 Sensitivity 93% Specificity 92% Accuracy 92%

Example 63 Circulating Vesicles Compared to Circulating Tumor Cells

Circulating Tumor Cells (CTCs) have been used to monitor disease progression in patients with different types of metastatic cancer. However, only 50% of metastatic breast, 57% of metastatic prostate, and 18% of metastatic colon cancer blood specimens have adequate levels of CTCs for clinical laboratory analysis. Levels of vesicles can correlate with tumor progression.

Methods:

Vesicles from 1 ml of plasma were isolated by ultracentrifugation. CD81 antibodies were used to capture and measure the vesicle level of breast cancer samples (n=14) and healthy controls (n=4). CTCs were measured for all samples using the Cell Search CTC test protocol. Subsequently, EpCam positive vesicles were captured from metastatic breast (n=10), prostate (n=2), and colon cancer (n=3) samples, and compared to healthy controls (n=7). RNA was extracted from these EpCam positive vesicles and microRNA-21 (miR-21) expression was quantified by qRT-PCR.

Results:

Eleven of the 14 samples (78.6%) had CD-81 specific vesicle levels significantly above the level found in the 4 healthy samples (p=0.002). See FIG. 117A. Only 7 of the 14 (50%) specimens analyzed had more than 5 CTCs, the clinical threshold for metastatic breast cancer. Three cancer samples had CD-81 measured vesicle levels below the average of normal samples, one of these had >5 CTCs. miR-21 analysis of 15 additional metastatic cancer specimens, 5 of which had >5 CTCs, found miR-21 averaged 4.2×10⁶, 4.82×10⁶ and 5.05×10⁶ copies in the breast, prostate, and colon cancer samples respectively. See FIG. 117B. Conversely, the plasma specimens from healthy donors collected in EDTA tubes averaged 1.8×10⁴ copies of miR21. See FIG. 117B.

Conclusions:

Vesicle analysis from plasma samples offer an opportunity to monitor and track disease, in some case better than CTC analysis. Tumor-derived vesicles provide the ability to characterize tumor of origin miR content, which demonstrates additional opportunities for tumor-specific vesicle-based biomarker analysis from a blood sample.

Example 64 Depletion of Vesicles from Plasma

Blood-based cancer diagnostic methods must filter through the vast array of biological molecules to select those few that are informative about a particular disease type from a specific organ. This presents many challenges especially when there is only a small amount of cellular material released into the blood stream. One strategy to overcome this obstacle is the selection and interrogation of circulating vesicles to learn about particular systems in the body. Vesicles comprise lipid bilayer encapsulated bodies such apoptotic bodies, blebs, exosomes, microvesicles and other biological entities as described herein. See Table 2 and related discussion. Microvesicles provide a rich source of information and are secreted by most cell types. Endothelial and leukocyte derived circulating microvesicles represent a majority of the circulating microvesicles present in the blood. This Example used depletion of these more common circulating microvesicles to allow for the enrichment and analysis of rarer subpopulations of microvesicles.

Magnetic beads were conjugated with CD31 and CD45 using methods described herein. The beads were incubated with human plasma from a breast cancer patient in order to deplete endothelial and leukocyte derived circulating microvesicles from the sample. The remaining microvesicles were characterized with a multiplexed immune based platform using capture antibodies to 20 different antigens simultaneously, according to methods described herein. See Examples 49-50. Some highly associated endothelial markers, e.g., DLL4, were significantly depleted along with CD31, while more general microvesicle markers, e.g., CD9, had significant populations remaining after depletion. See FIG. 118. These data indicate that specific populations of vesicles can be depleted from a patient sample. Similar trends were observed using magnetic beads to CD45 to deplete vesicles from the patient sample.

Example 65 Tissue Factor as a Vesicle Cancer Marker

Tissue factor is a blood clot-related protein whose expression has been noted in association with cancer. There are several biologic processes related to tumorigenesis or cancer progression that is tied to TF expression. These processes include angiogenesis, cancer cell invasion, immune evasion and circulating tumor cell survival. The fibrin clot that forms with TF expression coats cancer cells providing a protective coating for these cells. It is known that circulating TF is increased in the serum of cancer patients. Pathologic fibrotic events such as thromboembolism and stroke are major causes of cancer-associated deaths in patients and the existence of TF-expressing circulating microvesicles (cMVs).

FIG. 119 illustrates detection of Tissue Factor (TF) in vesicles from 10 normal (non-cancer) plasma samples, eight breast cancer (BCa) plasma samples and two prostate cancer (PCa) plasma samples. Vesicles in plasma samples were captured with anti-Tissue Factor antibodies tethered to microspheres as described herein. See Examples 49-50 for general methodology. The captured vesicles were detected with labeled antibodies to tetraspanins CD9, CD63 and CD81. The figure shows the median fluorescence intensity (MFI) observed by laser detection. The MFI of the BCa and PCa samples was consistently greater than the normal samples. The detection of Tissue Factor in diverse cancers indicates that TF can be used as a cancer vesicle marker.

Example 66 Selecting a Candidate Treatment for a Cancer

The methods of the invention can be used to identify a biosignature for theranosing a cancer. The biosignature can include any number of useful biomarkers, which can be assessed as described herein. The biosignature can be determined in a sample of bodily fluid, prefereably a blood sample, such as plasma or serum. Vesicles are obtained from sample of bodily fluid from a patient with a cancer using methodology presented herein. See Examples 49-50 for general methodology. For determining a biosignature for different settings, the appropriate biomarkers to include in the biosignature can be discovered as described above. See, e.g., section on Biosignature Discovery. The vesicles can be isolated, captured and/or assessed for surface antigens using a binding agent bound to a microsphere, such as described in Examples 48-50. The vesicles can also be isolated, captured and/or assessed for surface antigens using an array as in Examples 35-36, or FACS as in Example 26. Immunoassay techniques can also be used to capture vesicles. Biomarker payload within the isolated/captured vesicles can be analyzed as desired. The vesicles can be assessed for size using laser detection techniques.

The biosignature can further comprise additional biomarkers, such as microRNA. MicroRNA can be assessed directly from a bodily fluid or can be first isolated from a vesicle population. See, e.g., Example 12 (obtaining serum); Example 13 (RNA isolation from serum or plasma); Example 16 (extracting microRNA from vesicles). The microRNA can be assessed using RT-PCR (see Examples 14-15) and/or using array analysis (see Example 17). The microRNA can be analyzed using microfluidics to perform nucleic acid amplification.

The methods of identifying a biosignature can be performed in a single assay. For example, a number of biomarkers can be assessed using a multiplexed approach. In addition, some of the biomarkers can be assessed in a single assay while one or more other biomarkers are assessed in a different assay, which can also be a multiplexed assay. As an example, multiple vesicle surface biomarkers can be assessed in a first multiplex assay, and multiple microRNAs can be assessed in a second multiplex assay. The results of the first and second multiplex assays can be combined to identify a biosignature comprising the vesicle surface biomarkers and the microRNAs.

The biosignature can comprise any useful biomarker, including without limitation those presented herein in the context of various diseases and disorders, including without limitation markers for prostate cancer in Examples 8, 11, 28-42, 45-47 and 51; markers for colorectal cancer in Examples 9 and 52-58; markers for breast cancer in Examples 59-61 and markers for lung cancer in Examples 61-62.

Example 67 Treatment Associated Targets

The cancer is theranosed by identifying a biosignature including drug associated targets and prognostic markers. An advantage of this approach is that the sensitivity of the cancer to a candidate therapeutic can be determined without regard to the origin of the cancer. Rather, the molecular profile of the tumor itself provides a guide to therapeutic agent selection. A panel of antibodies or aptamers are used to assess a vesicle population for the presence or level of ABCC1, ABCG2, ACE2, ADA, ADH1C, ADH4, AGT, AR, AREG, ASNS, BCL2, BCRP, BDCA1, beta III tubulin, BIRC5, B-RAF, BRCA1, BRCA2, CA2, caveolin, CD20, CD25, CD33, CD52, CDA, CDKN2A, CDKN1A, CDKN1B, CDK2, CDW52, CES2, CK 14, CK 17, CK 5/6, c-KIT, c-Met, c-Myc, COX-2, Cyclin D1, DCK, DHFR, DNMT1, DNMT3A, DNMT3B, E-Cadherin, ECGF1, EGFR, EML4-ALK fusion, EPHA2, Epiregulin, ER, ERBR2, ERCC1, ERCC3, EREG, ESR1, FLT1, folate receptor, FOLR1, FOLR2, FSHB, FSHPRH1, FSHR, FYN, GART, GNRH1, GNRHR1, GSTP1, HCK, HDAC1, hENT-1, Her2/Neu, HGF, HIF1A, HIG1, HSP90, HSP90AA1, HSPCA, IGF-1R, IGFRBP, IGFRBP3, IGFRBP4, IGFRBP5, IL13RA1, IL2RA, KDR, Ki67, KIT, K-RAS, LCK, LTB, Lymphotoxin Beta Receptor, LYN, MET, MGMT, MLH1, MMR, MRP1, MS4A1, MSH2, MSH5, Myc, NFKB1, NFKB2, NFKBIA, NRAS, ODC1, OGFR, p16, p21, p27, p53, p95, PARP-1, PDGFC, PDGFR, PDGFRA, PDGFRB, PGP, PGR, PI3K, POLA, POLA1, PPARG, PPARGC1, PR, PTEN, PTGS2, PTPN12, RAF1, RARA, RRM1, RRM2, RRM2B, RXRB, RXRG, SIK2, SPARC, SRC, SSTR1, SSTR2, SSTR3, SSTR4, SSTR5, Survivin, TK1, TLE3, TNF, TOP1, TOP2A, TOP2B, TS, TUBB3, TXN, TXNRD1, TYMS, VDR, VEGF, VEGFA, VEGFC, VHL, YES1, ZAP70. The antibodies or aptamers can be bound to microspheres as in Examples 48-50 or arrayed as in Examples 35-36. These markers are known to play a role in the efficacy of various chemotherapeutic agents against proliferative diseases, see Table 69, and/or the prognosis of various cancers. Accordingly, the markers can be assessed to select a candidate treatment for the cancer independent of the origin or type of cancer, although the treating physician can take into account any other relevant information when selecting the candidate treatment, e.g., patient history, prior treatments, other testing results, cancer characteristics (e.g., stage, origin), physician experience, and the like.

TABLE 69 Biomarker - Drug Associations Biomarker Drug Associations ABCC1 (MRP1) doxorubicin (IHC and DMA), epirubicin (IHC and DMA), methotrexate (IHC and DMA), vincristine (IHC and DMA), vinorelbine (IHC and DMA), vinblastine (IHC and DMA), etoposide (IHC and DMA) ABCG2 (BCRP) cisplatin (IHC and DMA)), carboplatin (IHC and DMA) ADA pentostatin (DMA), cytarabine (DMA) ALK, EML4- crizotinib (FISH), pemetrexed (FISH) ALK AR bicalutamide (IHC and DMA), flutamide (IHC and DMA), abarelix (DMA), goserelin (DMA), leuprolide (DMA), gonadorelin (DMA) ASNS asparaginase (DMA), pegaspargase (DMA) BRCA1 mitomycin (DMA), cisplatin (DMA), carboplatin (DMA) BRCA2 mitomycin (DMA), cisplatin (DMA), carboplatin (DMA) CD52 alemtuzumab (IHC and DMA) CDA cytarabine (DMA) CES2 irinotecan (DMA) DCK gemcitabine (DMA), cytarabine (DMA) DHFR methotrexate (DMA), pemetrexed (DMA) DNMT1 azacitidine (DMA), decitabine (DMA) DNMT3A azacitidine (DMA), decitabine (DMA) DNMT3B azacitidine (DMA), decitabine (DMA) EGFR gefitinib (FISH and MA), erlotinib (FISH and MA), cetuximab (FISH and MA), panitumumab (FISH and MA) EPHA2 dasatinib (DMA) ERBB2 (HER2) trastuzumab (IHC and FISH), lapatinib (IHC and FISH), doxorubicin (FISH), epirubicin (FISH), liposomal-doxorubicin (FISH) ERCC1 cisplatin (IHC and DMA), carboplatin (IHC and DMA), oxaliplatin (IHC and DMA) ER tamoxifen (IHC and DMA), toremifene (DMA), fulvestrant (DMA), anastrozole (IHC and DMA), letrozole (IHC and DMA), exemestane (DMA), aminoglutethimide (DMA), megestrol (DMA), medroxyprogesterone (DMA) FLT1 (VEGFR1) bevacizumab (DMA), sunitinib (DMA), sorafenib (DMA) GART pemetrexed (DMA) HIF1A sunitinib (DMA), sorafenib (DMA) IGFBP3 letrozole (DMA) IGFBP4 letrozole (DMA) IGFBP5 letrozole (DMA) KDR (VEGFR2) sunitinib (DMA), sorafenib (DMA) Ki67 “tamoxifen + chemotherapy” (IHC) - breast only KIT (cKIT) sunitinib (MA and DMA), sorafenib (DMA), imatinib (MA and DMA), dasatinib (MA and DMA) KRAS gefitinib (MA), erlotinib (MA), cetuximab (MA), panitumumab (MA), sorafenib (MA), combination therapy (VBMCP) (MA) cMET/MET gefitinib (FISH), erlotinib (FISH) MGMT temozolomide (IHC and DMA) PDGFRA sunitinib (DMA), sorafenib (DMA) PDGFRB sunitinib (DMA), sorafenib (DMA) PGP (ABCB1) doxorubicin (IHC and DMA), liposomal doxorubicin (IHC and DMA), epirubicin (IHC and DMA), etoposide (IHC and DMA), teniposide (DMA), docetaxel (IHC and DMA), paclitaxel (IHC and DMA), vincristine (IHC and DMA), vinorelbine (IHC and DMA), vinblastine (IHC and DMA) PIK3CA/PI3K cetuximab (MA), panitumumab (MA), trastuzumab (MA) PR tamoxifen (IHC and DMA), toremifene (DMA), fulvestrant (DMA), anastrozole (IHC and DMA), letrozole (IHC and DMA), exemestane (DMA), aminoglutethimide (DMA), goserelin (DMA), leuprolide (DMA), gonadorelin (DMA), megestrol (DMA), medroxyprogesterone (DMA) PTEN erlotinib (IHC), gefitinib (IHC), cetuximab (IHC), panitumumab (IHC), trastuzumab (IHC) PTGS2 (COX2) celecoxib (IHC and DMA), aspirin (IHC) BRAF1 (BRAF) cetuximab (MA), panitumumab (MA) RARA ATRA (DMA) RRM1 gemcitabine (IHC and DMA), hydroxyurea (DMA) RRM2 gemcitabine (DMA), hydroxyurea (DMA) RRM2B gemcitabine (DMA), hydroxyurea (DMA) RXRB bexarotene (DMA) SPARC nab-paclitaxel (IHC and DMA) (mono/poly) SRC dasatinib (DMA) SSTR2 octreotide (DMA) SSTR5 octreotide (DMA) TLE3 paclitaxel (IHC), docetaxel (IHC) TOPO1/TOP1 irinotecan (IHC and DMA), topotecan (IHC and DMA) TOPO2A/TOP2A doxorubicin (IHC, FISH and DMA), liposomal doxorubicin (IHC, FISH and DMA), epirubicin (IHC, FISH and DMA) TOP2B doxorubicin (DMA), liposomal doxorubicin (DMA), epirubicin (DMA) TUBB3 paclitaxel (IHC), docetaxel (IHC), vinorelbine (IHC) TS/TYMS pemetrexed (IHC and DMA), capecitabine (DMA), fluorouracil (IHC and DMA) VDR choleciferol (DMA), calcitriol (DMA) VHL sunitinib (DMA), sorafenib (DMA)

The presence or level of each marker is compared to the presence or level of the same markers observed in a group of reference samples without the cancer. Biomarkers that are overexpressed or underexpressed in the patient sample compared to the reference samples are identified. A list is assembled of candidate therapeutic agents are that known to be effective against cancers that overexpress or underexpress the biomarkers identified using treatment-target associations as presented in Tables 10, 12-13, 69, and U.S. patent application Ser. No. 12/658,770, filed Feb. 12, 2010; International PCT Patent Application PCT/US2010/000407, filed Feb. 11, 2010; International PCT Patent Application PCT/US2010/54366, filed Oct. 27, 2010; and U.S. Provisional Patent Application 61/427,788, filed Dec. 28, 2010; all of which applications are incorporated by reference herein in their entirety. See, e.g., “Table 4: Rules Summary for Treatment Selection” of PCT/US2010/54366. The treating physician is presented a report comprising the expression levels of the biomarkers assessed and the list of drug indications. The physician uses the report to aid in selection of a candidate treatment.

Example 68 Monitoring Treatment Efficacy of Prostate Cancer

Methods for detecting a vesicle biosignature for prostate cancer are described in Examples 29-32. Vesicles are detected in a blood sample from a patient. The biosignature is determined by detecting the presence of the following vesicle surface antigens in the sample:

-   -   a. General Vesicle (MV) markers: CD9, CD81, and CD63     -   b. Prostate MV markers: PCSA, PSMA     -   c. Cancer-Associated MV markers: B7H3, optionally EpCam

The biosignature is used to monitor an efficacy of a treatment for the prostate cancer. A patient is identified with a suspicious serum PSA level (e.g., serum PSA >4.0 ng/ml) and/or a suspicious digital rectal examination (DRE). The vesicle biosignature is determined for the patient, and the results are found to be positive for prostate cancer. The treating physician determines whether to treat the prostate cancer with a therapeutic agent, hormone therapy, or a surgery (prostatectomy). After treatment, the vesicle biosignature is again determined for the patient. A positive result for cancer indicates a negative patient response to the treatment and that further treatment is required. A negative result indicates a positive patient response to the treatment and that further treatment may not be necessary.

Alternate biosignatures for prostate cancer can also be used, including without limitation those presented in Examples 8, 11, 28-42, 45-47 or 51. Similar methodology is used to monitor treatment efficacy of other diseases and disorders. For example, colorectal cancer treatment can be monitored using a biosignature as described in Examples 9 or 52-58, breast cancer treatment can be monitored using a biosignature as described in Examples 59-61, and lung cancer treatment can be monitored using a biosignature as described in Examples 61-62. Biosignatures for these cancers and other diseases and disorders as presented herein can also be used to monitor treatment efficacy.

Example 69 Detecting Breast Cancer Using Circulating Microvesicles (cMVs)

Vesicles were detected in plasma following general methodology as presented in Examples 49-50. Breast cancer (BCa) and normal plasma samples were analyzed using bead-based capture antibodies and labeled detector antibodies. Vesicles were captured using antibodies to the biomarkers CD9, HSP70, Gal3, MIS (RII), EGFR, ER, ICB3, CD63, B7H4, MUC1, DLL4 (R23), CD81, ERB3, MART1, STAT3, DLL4 (R34), VEGF, DLL4 (R45), BCA225, BRCA, CA125, CD174, DLL4 (R63), CD24, ERB2, NGAL, GPR30, CYFRA21, DLL4 (R80), DLL4 (R81), DLL4 (R82), DLL4 (R83), DLL4 (R84), DLL4 (R85), CD31, cMET, VEGF (R2), MUC2 and ERB4. Several different antibodies to DLL4 were used, as indicated (i.e., R23-DLL4, R34-DLL4, R45-DLL4, R63-DLL4, and R81-R85-DLL4). Two VEGF antibodies were used as well. The vesicles were detected using PE labeled antibodies to the tetraspanins CD9, CD63 and CD81. The tetraspanins are general vesicle markers and are used to detect all captured vesicles. The values for CD9, CD63 and CD81 capture were in the range typically observed in normal samples, indicating that the plasma samples were not degraded. The same vesicles were also detected using a labeled antibody to CD31, which is an endothelial marker. Thus, the vesicles detected with anti-CD31 are presumed to be endothelial in origin.

Generally, the vesicles captured using antibodies to the above biomarkers were found at significantly different levels between normals and BCa samples. However, when using R85-DLL4 and cMET capture antibodies, there was a significant difference between normal and cancer when labeling with the tetraspanin cocktail (CD9, CD63 and CD81), but there was no significant difference between normal and cancer when labeling with anti-CD31. Taken together, these data suggest that the cMVs detected with R85-DLL4 and cMET are released in response to the tumor.

Example 70 Flow Cytometry to Detect and Sort Vesicles

FIG. 120 shows flow sorting of vesicles labeled with FITC-conjugated antibodies to the indicated vesicle antigens. General methodology for flow sorting vesicles is presented in Example 26 above. In this Example, vesicles isolated from a blood sample from a subject were incubated with FITC labeled antibodies to the tetraspanins CD9 and/or CD63 as indicated. The tetraspanins are general vesicle markers and identify vesicles in the sample apart from other debris in the sample. The vesicles are simultaneously incubated with phycoerythrin (PE) or Cy7 labeled antibodies to CD31, DLL4, and/or TMEM211 as indicated. CD31 identifies endothelial derived vesicles, DLL4 is an angiogenic maker and TMEM211 is a colorectal marker. The vesicle particles identified by the anti-tetraspanin antibodies are flow sorted to detect the levels of vesicles in the samples that express CD31, DLL4, and/or TMEM211.

FIG. 120A shows CD9/CD63 FITC-labeled vesicles from a colorectal cancer (CRC) patient and a normal subject (i.e., without CRC) gated for CD31 and DLL4 levels. These data reveal that CD31+ endothelial derived vesicles display similar levels of DLL4 in the CRC and normal subjects. FIG. 120B shows CD9/CD63 FITC-labeled vesicles from a normal and CRC patient gated for TMEM211 and DLL4 levels. These data reveal that TMEM211+ colorectal vesicles display much higher levels of the angiogenic marker DLL4 in the CRC subjects (45% DLL4+) versus normal subjects (2% DLL4+). FIG. 120C shows CD9 FITC-labeled vesicles from a normal and breast cancer patient gated for CD31 and DLL4 levels. Like the CRC patient shown in FIG. 120A, these data reveal that endothelial derived vesicles (i.e., CD31+ vesicles) display similar levels of DLL4 in the breast cancer and normal subjects.

Example 71 DLL4+ Circulating Microvesicles in Various Cancers

Vesicles were detected in plasma following the methodology as presented in Examples 49-50. Microvesicles in cancer samples and normal (i.e., non-cancer) samples were captured using bead-tethered anti-DLL4 antibodies. The captured cMVs were labeled with PE-labeled detector antibodies to the tetraspanins CD9, CD63 and CD81. The median fluorescence values (MFI) of the captured and labeled cMVs were calculated. The numbers of normal and cancer samples are shown in Table 70.

The calculated MFI for all sample groups is shown in Table 70 and FIG. 121. The mean MFI was compared between normal controls and various cancers. The mean MFIs in all cancers were higher than in normals. The large standard deviation in the normals was due to four of the 36 samples having much greater MFI (from 1250 to 2600; data not shown). The median MFI in normals was 51.13. An MFI threshold was set to distinguish each group of cancers against the normal control group. Results are shown in Table 70. The results indicate that levels of DLL4+ vesicles can be used to distinguish a variety of cancers from normal, non-cancer controls.

TABLE 70 DLL4+ cMVs are Differentially Expressed in Cancer Sample Type Sample Number Mean MFI MFI Deviation MFI Cutoff Sens Spec Accuracy Normal 36 284.41 558.73 106 78.80% 72.20% 77.2 Breast Cancer 20 632.69 158.51 92 80.00% 66.70% 71.4 Lung Cancer 20 620.58 52.50 68 90.00% 58.30% 69.6 Prostate Cancer 20 918.95 180.12 559 60.00% 83.30% 75.0 Colorectal 19 1426.53 239.78 284 89.50% 80.60% 83.6 Cancer Renal Cancer 8 1654.25 121.21 738 87.50% 88.90% 88.6 Ovarian Cancer 13 1399.17 226.29 109 84.60% 72.20% 75.5 Pancreatic 13 924.38 159.32 166 76.90% 75.00% 75.5 Cancer

Example 72 MicroRNA for Detecting Lung Cancer

MicroRNA was examined in RNA isolated from plasma samples. RNA was extracted from plasma of five lung cancer samples and five normal controls (non-lung cancer) using the MagMAX™ RNA Isolation Kit from Applied Biosystems/Ambion, Austin, Tex. to extract microRNA as detailed in Example 16.

MicroRNA in the samples was examined using the Exiqon mIRCURY LNA microRNA PCR system panel (Exiqon, Inc., Woburn, Mass.). The Exiqon 384 well panels measure 750 miRs. Samples were normalized to control primers towards synthetic RNA spike-in from Universal cDNA synthesis kit (UniSp6 CP). Normalized values for each probe across three data sets for each indication (BPH or PCa) were averaged. Probes with an average CV % higher than 20% were not used for analysis.

Expression data for the miRs was compared between the lung cancer and normal using a t-test. Table 71 shows the results of the analysis for miRs with a significant p-value (p=0.05). Fold change (FC) in the expression between groups is also shown. hsa-miR-125a-3p has been reported to be downregulated in non-small cell lung cancer. Jiang et al., Hsa-miR-125a-3p and hsa-miR-125a-5p are downregulated in non-small cell lung cancer and have inverse effects on invasion and migration of lung cancer cells. BMC Cancer. 2010 Jun. 22; 10:318.

TABLE 71 miR Expression in Lung Cancer versus Normal Regulation in Normal miR p-value Fold-change compared to LCa hsa-miR-125a-5p 0.004 41.07 Up hsa-miR-650 0.005 2.192 Down hsa-miR-194 0.005 4.474 Down hsa-miR-1200 0.006 5.890 Up hsa-miR-326 0.006 24.782 Up hsa-miR-30b* 0.007 19.216 Up hsa-miR-19a 0.008 7.548 Up hsa-miR-7a* 0.011 9.459 Down hsa-miR-708* 0.012 2.474 Up hsa-miR-99a 0.013 4.771 Up hsa-miR-199b-5p 0.013 4.971 Up hsa-miR-543 0.013 9.036 Up hsa-miR-7i* 0.013 7.537 Down hsa-miR-518c* 0.015 4.660 Up hsa-miR-642 0.016 12.227 Up hsa-miR-654-3p 0.021 5.706 Down hsa-miR-518d-5p 0.023 5.765 Up hsa-miR-1266 0.027 2.706 Up hsa-miR-154 0.028 7.046 Down hsa-miR-662 0.032 6.624 Up hsa-miR-523 0.034 7.268 Up hsa-miR-198 0.035 6.776 Up hsa-miR-920 0.045 3.106 Up hsa-miR-885-3p 0.046 2.160 Up hsa-miR-99a* 0.047 3.855 Up hsa-miR-337-3p 0.049 3.740 Down hsa-miR-363 0.049 4.170 Down

Example 73 MicroRNA miR-497 for Detecting Lung Cancer

There is currently no blood test for the early diagnosis of lung cancer. MicroRNA was examined in circulating microvesicles (cMVs) isolated from plasma samples. Vesicles were isolated and assessed generally as described in Examples 49-50. RNA was extracted from the vesicles contained in 1 ml of plasma using a Trizol method. MicroRNA payload was detected using quantitative Taqman® RT-PCR methodology. The expression of miR-497 was examined in plasma from 16 lung cancer patients and 15 control normal adults (i.e., no lung cancer). A significant difference in the copy number of miR-497 was observed between the two groups (p=0.0001). See FIG. 122A. Using a threshold of 1154 copies of miR-497 (in 0.1 ml of plasma) to differentiate lung cancer versus normal samples (indicated by the vertical line in FIG. 122A), lung cancer was detected with 88% sensitivity and 80% specificity.

In a follow on study, circulating microvesicles (cMVs) from 24 non-small cell lung cancer (NSCLC) patients of primarily early stage disease (Stage IA=9, IB=9, IIA=1, IIB=2, III=1, IV=2) and 26 healthy individuals were isolated from 1 ml of frozen plasma. The expression of miR-497 was examined in the cMVs from plasma samples from the lung cancer patients and 26 control normal adults (i.e., no lung cancer). Patient characteristics are shown in Table 72.

TABLE 72 Patient Characteristics Stage Males Females Stage IA 5 4 Stage IB 4 5 Stage IIA 1 0 Stage IIB 1 1 Stage III 1 0 Stage IV 0 2 Normal 14 12

Median normalized copy number was 9000±307 copies per ml (±95% CIM) for normal individuals and 27,500±1298 copies per ml (±95% CIM) for patients with NSCLC. Setting a threshold for cancer of 1570 copies in 0.1 ml samples (i.e., 15,700 copies per ml), the assay had a sensitivity of 79% and specificity of 81% and an AUC of 0.89. See results in FIGS. 122B-122C and Table 73. Table 73 shows test performance using cut off thresholds of 13,560 and 15,700 copies/ml. The threshold can be adjusted to favor sensitivity or specificity.

TABLE 73 miR-497 to Detect of Lung Cancer True Positive 21 19 True Negative 18 21 False Positive 8 5 False Negative 3 5 Sensitivity 88% 79% Specificity 69% 81% Accuracy 78% 80% AUC 0.89 0.89 Cut off 13,560 15,700 (copies/ml)

Example 74 Circulating Microvesicle Biosignature for Lung Cancer

Lung cancer is a highly lethal disease with approximately 85% of patients dying of their disease within five years of diagnosis. The best outcomes are observed when the cancer is detected at earlier stages. A typical strategy for detecting early lung cancer involves advanced imaging strategies such as chest radiography, CT or MRI studies. Unfortunately, these approaches result in high false positive rates approaching 21% for CT. In a high risk group of current or former heavy smokers, between 4% and 7% of patients underwent unnecessary invasive procedures with resulting economic, psychosocial and medical burdens.

A circulating microvesicle (cMV) biosignature was developed for the detection of lung cancer in plasma sample. Twenty biopsy-confirmed NSCLC plasma samples and 25 normal (i.e., non-lung cancer) age-matched control plasma samples were analyzed using 100 different capture antibodies bound to microspheres as described herein. See Example 61. The NSCLC samples analyzed were comprised of AJCC/UICC stage IA/B (n=7), IIA (n=4), IIB (n=7) and IIIA (n=2). Half of these patients were positive for lymph node involvement. The control cohort included smokers and non-smokers. Analysis was also performed with 13 additional patients with confounder diseases (6 diabetes and 7 rheumatoid arthritis) in the non-cancer population. These confounder conditions/diseases are likely to be encountered in the clinic in non-cancer patients, so clinical utility includes the test's performance in the most likely population of screening patients. The concentrated cMVs were then mixed with capture-conjugated microspheres, washed and quantified with a cocktail of tetraspanins (CD9, CD81, CD63) conjugated to PE. Captured cMVs were quantified using the secondary fluorochrome-conjugated tetraspanin detection antibodies. The MFIs for each microsphere subtype was analyzed to determine the best test to identify lung cancer patients. A sub-panel of markers and fluorescence level cut-offs for each were determined to optimize the accuracy of the biosignature for detecting lung cancer.

A biosignature of 6 different proteins was found that differentiated the NSCLC patients from individuals from the normal control population with a sensitivity of 85%, specificity of 92% and accuracy of 89%, as shown in Table 74. The biosignature comprises 6 different surface membrane protein markers, which include both microvesicle and cancer-associated proteins. Three of the proteins are members of the tetraspanin transmembrane family (CD9, CD63 and CD81) that are found on microvesicles. Three other protein markers are DR3 (death receptor 3, a protein involved in apoptosis), PRB (progesterone receptor B) and MS4A1 (Membrane-spanning 4 domain subfamily A from the multigene family of proteins involved in signal transduction of which MS4A1 (CD20) is one member). In these experiments, fluorescently labeled antibodies to the tetraspanins were used as detector binding agents and bead-conjugated antibodies to the other proteins were used as capture agents. Importantly, smoking did not lead to any false positive calls; the only two false positives in this cohort were normal non-smoking patients. When the common confounder diseases were included in the analysis, the specificity and accuracy were 84%, as shown in Tables 74-75.

TABLE 74 cMV Detection of Lung Cancer MS4A1 and (PRB MS4A1 PRB DR3 or DR3) True Positive 17 16 18 17 True Negative 23 22 22 23 False Positive 2 3 4 2 False Negative 3 4 2 3 Total 45 45 45 45 Sensitivity 85% 80% 90% 85% Specificity 92% 88% 84% 92% Accuracy 89% 84% 87% 89% Cutoff 194 400 400

TABLE 75 cMV Detection of Lung Cancer with Confounders MS4A1 and (PRB MS4A1 PRB DR3 or DR3) True Positive 17 16 18 17 True Negative 30 31 30 31 False Positive 8 7 8 6 False Negative 3 4 2 3 Total 58 58 58 58 Sensitivity 85% 80% 90% 85% Specificity 79% 82% 79% 84% Accuracy 81% 81% 83% 84% Cutoff 194 400 400

MACC1 provided similar results to PRB in the sample analysis, as shown in Table 76.

TABLE 76 cMV Detection of Lung Cancer PRB MACC1 PRB + MACC1 True Positive 16 16 16 True Negative 23 23 23 False Positive 2 2 2 False Negative 3 3 3 Total 44 44 44 Sensitivity 84% 84% 84% Specificity 92% 92% 92% Accuracy 89% 89% 89% Cutoff 447.75 571.125

Example 75 Elevated Levels of CD9+ Exosomes in Cancer

Antibodies to the general vesicle antigen CD9 were tethered to beads and used to capture vesicles in plasma samples from 1706 subjects with various cancers or normals (i.e., non-cancer). Methodology is generally as presented in Examples 49-50. The CD9 bead captured vesicles were detected with fluorescently labeled antibodies against the tetraspanins CD9, CD63 and CD81. The median fluorescence intensity (MFI) of the captured and labeled vesicles was measured using laser detection. Table 77 shows a breakdown of the sample types, sample count for each sample type and the average MFI for each group of samples. The corresponding data is presented graphically in FIGS. 123A and 123B. As shown in FIG. 123A, the cancers as a group had about a three-fold increase in MFI as compared to the normals. FIG. 123B illustrates that the MFIs were elevated across numerous unrelated cancers as compared to normals.

TABLE 77 MFI of CD9 captured vesicles in plasma from cancer patients Average Cancer MFI Count Normal (non-cancer) 1403.162 673 Prostate 3296.627 542 Lung 7270.545 100 Colon 4728.402 362 Breast 3700.917 9 Bladder 5257.75 1 Endometriod 3451.313 4 Liver 7947.25 1 Pancreatic 9589.75 3 Ovary 1135 2 Esophagus 6419.125 2 Kidney 2263.643 7

Example 76 Detecting Breast Cancer (BCa) with Vesicle Marker Combinations

Antibodies to a number of antigens of interest were tethered to beads and used to capture vesicles in plasma samples from subjects with breast cancer or normal controls (i.e., no breast cancer) as described herein. See, e.g., Examples 59-60. The bead captured vesicles were detected with fluorescently labeled antibodies against the tetraspanins CD9, CD63 and CD81. The mean fluorescence intensity (MFI) of the captured and labeled vesicles was measured using laser detection. The sample cohort included 80 breast cancer patients and 34 age-matched controls. The BCa patients included 22 Stage I, 28 Stage II, 28 Stage III and one Stage IV breast cancers.

Results using capture antibodies to the biomarkers Gal3 and BCA200 are shown in FIGS. 124A-E and Tables 78-82. The anti-BCA200 antibody is Catalog number B2708-06, “Anti-BRCA, 40/60/100/200 kD Glycoprotein Complex (Breast Cancer Antigen, Early Onset Breast Ovarian Cancer Susceptibility Protein),” from United States Biological, Swampscott, Mass. The antibody recognizes human BCA200, a complex glycoprotein antigen from breast cells. The antigen can also be referred to as BRCA (see US Biological product information). In cancer cells the distribution of this complex is different than normal cells. In FIG. 124A, mean fluorescence values (MFIs) for vesicles detected using the Gal3 and BCA200 capture antibodies are shown. The vertical line shows the MFI cutoff used for Gal3 to differentiate BCa and normal and the horizontal line shows the cutoff used for BCA200 to differentiate BCa and normal. Table 78 shows the numerical results obtained. As shown in Table 78, when using both markers (labeled as “Both”), 3 false positives and 11 false negatives were observed. Of the false negatives, three were Stage I, three were Stage II, and five were Stage III breast cancers.

TABLE 78 Detection of breast cancer with Gal3 and BCA200 Gal3 BCA200 Both True Positive 76 72 69 True Negative 28 25 31 False Positive 6 9 3 False 4 8 11 Negative Total 114 114 114 Sensitivity 95.0% 90.0% 86.3% Specificity 82.4% 73.5% 91.2% Accuracy 91.2% 85.1% 87.7% Cutoff 500 300

FIG. 124B and Table 79 are the same as FIG. 124A and Table 78, respectively, except that the cutoffs for each marker are adjusted to favor sensitivity over specificity, with a concomitant increase in accuracy when using the combination of both markers. A comparison of Tables 78 and 79 demonstrate how test performance can be adjusted to favor sensitivity or specificity. For example, a more sensitive test may be favored for screening applications in order to identify subjects who should undergo follow on tests (e.g., biopsy, mammogram, colonoscopy, etc). A more specific test may be preferable in a setting where it is more important to rule out the presence of a disease.

TABLE 79 Detection of breast cancer with Gal3 and BCA200 Gal3 BCA200 Both True Positive 76 79 75 True Negative 30 17 31 False Positive 4 17 3 False 4 1 5 Negative Total 114 114 114 Sensitivity 95.0% 98.8% 93.8% Specificity 88.2% 50.0% 91.2% Accuracy 93.0% 84.2% 93.0% Cutoff 550 180

In the study, 28 of the breast cancer cases were diagnosed as lobular carcinoma. Of these 28 cases, 25 were correctly diagnosed in this assay for a sensitivity of ˜90% for lobular carcinoma.

FIG. 124C and Table 80 present data similar to that shown above except that 37 confounder samples were included in the analysis. The confounders had inflammatory diseases or diseases known to induce strong immune responses including diabetes, diverticulitis, chronic obstructive pulmonary disease (COPD), and asthma.

TABLE 80 Detection of breast cancer with Gal3 and BCA200 with Confounder samples Gal3 BCA200 Both True Positive 76 72 69 True Negative 39 32 43 False Positive 32 39 28 False 4 8 11 Negative Total 151 151 151 Sensitivity 95.0% 90.0% 86.3% Specificity 54.9% 45.1% 60.6% Accuracy 76.2% 68.9% 74.2% Cutoff 500 300

As seen by comparing the accuracy observed in Table 78 versus Table 80, accuracy decreased with inclusion of the confounder samples. This decrease was primarily due to increased numbers of false positives, leading to decreased specificity. The specificity can be improved using additional biomarkers. FIG. 124D and Table 81 show the differentiation of the breast cancer and confounder samples described above using NCAM and OPN. Neural cell adhesion molecule 1 (NCAM-1, NCAM), also known as CD56, is a calcium-dependent adhesion molecule in the Ig superfamily. Osteopontin (OPN), also designated bone sialoprotein 1, urinary stone protein, SPP-1, ETA-1, nephropontin and uropontin, is an extracellular matrix cell adhesion phosphoglycoprotein.

TABLE 81 Distinguishing breast cancer and confounder samples with OPN and NCAM OPN NCAM Both True Positive 61 60 58 True Negative 17 16 18 False Positive 8 9 7 False 8 9 11 Negative Total 94 94 94 Sensitivity 88.4% 87.0% 84.1% Specificity 68.0% 64.0% 72.0% Accuracy 83.0% 80.9% 80.9% Cutoff 182.5 650

FIG. 124E illustrates a two-step procedure for distinguishing breast cancer by combining the results obtained using Gal3, BCA200, OPN and NCAM. First, Gal3 and BCA200 are used to distinguish the samples as shown in the leftmost plot. The samples in the quadrant marked “Positive” are then assessed using OPN and NCAM as shown in the rightmost plot to separate false positive confounder patients. The results obtained with the two step approach are shown in Table 82. As seen in the table, the addition of the second step improves the specificity from 59.7% to 70.8%, and the accuracy from 77.5% to 80.1%.

TABLE 82 Distinguishing breast cancer with a 4 marker approach With OPN and Gal3 BCA200 Both NCAM True Positive 75 78 74 70 True Negative 41 22 43 51 False Positive 31 50 29 21 False 4 1 5 9 Negative Total 151 151 151 151 Sensitivity 94.9% 98.7% 93.7% 88.6% Specificity 56.9% 30.6% 59.7% 70.8% Accuracy 76.8% 66.2% 77.5% 80.1% Cutoff 550 180

Example 77 Circulating Microvesicles (cMVs) Correlate with the Presence of Circulating Tumor Cells (CTCs) in Patients with Solid Tumors

As described herein, circulating microvesicles (cMVs) can be assessed from various body fluids including plasma or serum. Blood-based assays for the detection of circulating tumor cells (CTCs) are well established but prognostic significance of CTCs has not been established and is currently debated. The presence of CTCs may indicate a greater likelihood of future metastasis and/or disease progression in patients with solid tumors.

cMVs and CTCs are compared in breast cancer patients. Various vesicle surface antigens are assessed using bead-based assay and/or for direct multiparametric phenotyping using flow cytometry. Flow sorted or captured vesicles are further assessed for vesicle payload markers (e.g., microRNA, mRNA and proteins). A vesicle marker profile is identified that predicts the presence of CTCs with greater sensitivity and greater correlation to eventual disease progression than conventional CTC analysis. This panel of proteins and/or nucleic acid molecules is detected in cMVs of cancer patients using methods of isolation and evaluation methods and markers as described herein.

Marker profiles are examined in sequential blood collections from more than 250 newly diagnosed breast cancer patients before, during and after treatment for their disease with follow-up clinical data for relapse. Plasma samples are evaluated with the full discovery panel of the breast cancer study and correlated with presence/absence of CTCs as well as eventual treatment response and/or eventual relapse.

Example 78 Circulating Microvesicles (cMVs) to Evaluate Cancer Stem Cells in Patients with Breast Cancer

Cancer stem cells are described as cancer cells with unlimited replicative potential and the ability to form new tumors in recipient animals from a single cell. Such tumors can be heterogeneous with regard to specific protein and/or nucleic acid (mutations, mRNA and microRNA) expression.

Relative cancer stem cell frequencies with regard to non-stem cell breast cancer cells may change while a patient is undergoing therapeutic treatment. Additionally, novel therapies are currently undergoing trials that specifically target cancer stem cells such as inhibitors of the sonic hedge hog (SHH) pathway. A blood based test using cMVs to monitor and predict response in a non-invasive fashion can therefore be used to monitor these cancer stem cells with prognostic implications for patients undergoing therapy for breast cancer.

Various membrane protein markers have been identified on the surface of breast cancer stem cells including CD44 and EpCam. The cells have been further found to have a corresponding lack of expression of CD24. Using multiparametric flow cytometry and/or bead-based assays as described here, cMVs are evaluated in plasma from patients suffering from breast cancer. The cMVs are assessed using an antibody panel with appropriate fluorochromes including the markers CD44, CD9, EpCam, CD24 and breast cancer specific markers including Muc1, BCA200 and Gal3 as described herein. These results are correlated with the presence or absence of breast cancer stem cells within the tumors of the patients.

Example 79 Plasma-Derived Microvesicles in Breast Cancer

Circulating microvesicles play an important role in several biologic processes, including angiogenesis and immune modulation. This Example looked at the level of specific microvesicles subpopulations that are altered in patients with a disease, specifically breast cancer. Monitoring microvesicle subpopulations will help identify important biologic processes associated with the progression of cancers and other diseases.

To identify cancer associated microvesicles, microvesicles in patient samples were compared between a cohort of patients with advanced breast cancer versus normal controls without breast cancer. Microvesicles (MVs) in plasma samples from the cohort were concentrated, stained with fluorochrome conjugated antibodies and analyzed using flow cytometry. Tumor-specific, leukocyte-specific and stromal cell-specific antibodies were used to identify and characterize these subtypes of microvesicles in the plasma samples. These tissue-specific antibodies were paired with process-specific markers such as DLL4 and VEGFR2 for angiogenic microvesicles, CTLA4 and FasL for immunosuppressive microvesicles, and CD80 and CD83 for immunostimulatory microvesicles. The vesicles were detected using labeled capture antibodies to the markers to label the vesicles, then flow cytometry was used to detect the labeled vesicles as described herein.

Plasma-derived cMVs from 5 women with advanced-stage breast cancer (Stage III/IV) and 4 healthy women were labeled with panels of fluorochrome-conjugated antibodies according to the indicated panels in Table 83. In the leftmost column of the table, NP references a normal plasma sample and BC references a plasma sample from a BCa positive patient. The stained cMVs were analyzed using a Beckman-Coulter Mo-Flow XDP (Beckman Coulter, Inc., Brea, Calif., USA). Four-color staining was used to evaluate immunosuppressive cMVs (Tetraspanin⁺, CD45⁺, FasL⁺, CTLA4⁺), angiogenic cMVs (Tetraspanin⁺, CD31⁺, DLL4⁺, VEGFR2⁺, HIF2a⁺, Tie2⁺, Ang1⁺) and metastatic cMVs (tetraspanin⁺, CD147⁺, TIMP1⁺, TIMP2⁺, MMP7⁺, MMP9⁺). Exemplary results for individual patients are shown in FIGS. 125A-125C. Results were calculated as the percentage of positively stained particles as well as number of positive particles per μl of plasma (Table 83).

TABLE 83 Distinguishing breast cancer with a 4 marker approach Marker % CD83+/ CTLA4+/ CD147+/ TIMP2+/ HIF2a+/ VEGFR2+/ (#/ul plasma) FasL+ CD80+ TIMP1+ MMP9+ Ang1+ Tie2+ NP4  43%  1.3%   0.3% 0.5%   5%   6%  0.1% (1,023) (29) (1) (4) (13) (63) (1) NP5  92%  0.1%   0.2% 0.3%   3%   4%  0.7% (3,895) (1) (4) (16) (115) (183) (35) NP6  75%  0.1%   0.1% 0.1% 0.5% 1.8%  0.1% (2,068) (4) (4) (7) (23) (90) (2) NP9  83%  1.2% 0.03% 0.1%   1% 0.6% 0.05% (3,364) (56) (2) (5) (32) (18) (2) Ave Normal 73.3%  0.7%   0.2% 0.3% 2.4% 3.1%  0.2% (2,587.5) (22.5) (2.8) (8) (45.8) (88.5) (10) BC1  98%   3%   0.4% 0.2% 1.5% 1.6%  0.2% (3,778) (15) (1) (3) (48) (53) (6) BC2  59%   7%   0.5% 0.3%   4% 1.5%  0.3% (2,301) (59) (5) (10) (124) (50) (5) BC3  72%   5%   0.5% 0.1%   5%   2%   1% (5,377) (105) (4) (7) (236) (131) (79) BC7  33%   5%   0.1% 0.6%   5%   1%   1% (932) (104) (4) (14) (122) (32) (4) BC8  85%  47%   5%   2%   1%   2%   15% (3,462) (3,150) (263) (110) (5) (127) (1016) Ave Br. 69.4% 13.4%   1.3% 0.6% 3.3 1.6%  3.5% Cancer (3,170) (686.6) (55.4) (28.8) (107) (78.6) (222)

Flow cytometry was used to compare the protein expression of cMVs in breast cancer patients vs. controls. This staining is analogous to phenotyping of cells to evaluate these same processes in vivo. Phenotyping studies showed that typical “percentage positive” analysis was not useful to identify subpopulations of biologically relevant cMVs (Table 83).

However, cancer patients tend to have elevated levels of cMVs compared to healthy controls so these percentage differences are magnified when we compare the total number of events in 1 μl of plasma. For example the number of immunosuppressive cMVs derived from DCs (CD83+/FasL+) averages 22.5/μl in healthy controls and 686.6/μl in the breast cancer patients. Similarly 8 metastatic (CD147+/TIMP1+) cMVs/μl were detected in healthy volunteers compared to 28.8/μl in the breast cancer plasma. Finally angiogenic cMVs (VEGFR2+/Tie2+) increased from 10/μl to 222/μl in breast cancer plasma. See Table 83.

Circulating microvesicles were compared between the breast cancer patients and healthy controls. Distinct and informative subpopulations were identified and quantified by probing antigens associated with the vesicles. Immunosuppressive microvesicles were elevated in the cancer patients (68% vs. 44% of CD45⁺ MVs co-expressed CTLA4). Additionally, angiogenic MVs were elevated in the cancer patients' plasma, with 44% of circulating microvesicles co-expressing DLL4 and CD31 compared with 2% of vesicles displaying these markers in the normal controls.

The results of these studies demonstrate increased percentage and number of immunosuppressive, angiogenic and metastatic cMVs in late stage breast cancer patients compared with healthy women Immunosuppressive cMVs were >700-fold higher, metastatic cMVs were >3-fold higher and angiogenic cMVs were >21-fold higher by volume of plasma in advanced breast cancer plasma compared to controls. Circulating microvesicles can provide a simple and reliable tool to obtain important information about malignant and cancer progression processes occurring in a patient without the need for a biopsy or standard pathology evaluation such as immunohistochemistry.

Example 80 Breast Cancer cMV Protein Biomarkers

In this Example, capture antibodies were screened for their ability to identify breast cancer circulating microvesicles (cMVs) in plasma samples and breast cancer cell line MCF7. General methodology is as presented in Examples 49-50. Capture antibodies included antibodies to 5T4 (trophoblast), ADAM10, AGER/RAGE, APC, APP (β-amyloid), ASPH (A-10), B7H3 (CD276), BACE1, BAI3, BRCA1, BDNF, BIRC2, C1GALT1, CA125 (MUC16), Calmodulin 1, CCL2 (MCP-1), CD9, CD10, CD127 (IL7R), CD174, CD24, CD44, CD63, CD81, CEA, CRMP-2, CXCR3, CXCR4, CXCR6, CYFRA 21, derlin 1, DLL4, DPP6, E-CAD, EpCaM, EphA2 (H-77), ER(1) ESR1α, ER(2) ESR2 β, Erb B4, Erbb2, erb3 (Erb-B3) PA2G4, FRT (FLT1), Gal3, GPR30 (G-coupled ER1), HAP1, HER3, HSP-27, HSP70, IC3b, IL8, insig, junction plakoglobin, Keratin 15, KRAS, Mammaglobin, MART1, MCT2, MFGE8, MMP9, MRP8, Muc1, MUC17, MUC2, NCAM, NG2 (CSPG4), Nga1, NHE-3, NT5E (CD73), ODC1, OPG, OPN, p53, PARK7, PCSA, PGP9.5 (PARKS), PR(B), PSA, PSMA, RAGE, STXBP4, Survivin, TFF3 (secreted), TIMP1, TIMP2, TMEM211, TRAF4 (scaffolding), TRAIL-R2 (death Receptor 5), TrkB, Tsg 101, UNC93a, VEGF A, VEGFR2, YB-1, VEGFR1, GCDPF-15 (PIP), BigH3 (TGFb1-induced protein), 5HT2B (serotonin receptor 2B), BRCA2, BACE 1, CDH1-cadherin. Antibodies are listed in Table 84. In addition to PE labeled anti-tetraspanin detectors (i.e., anti-CD9, anti-CD63 and anti-CD81), PE labeled anti-MFGE8 was also used as a detector antibody.

TABLE 84 Capture Antibodies Antibody Antigen Clone Vendor Cat# Anti Interleukin 8 antibody IL8 (5D21) IL8 5D12 US Biologicals I8430-06A Anti trefoil factor 3 (intestinal) antibody (clone TFF3 (secreted) 3D9 Sigma WH0007033 3D9) M1 PGP9.5 (clone 3D9) antibody PGP9.5 3D9 Genway 20-002- 35062 Anti survivin antibody (5B10) Survivin 5B10 Sigma WH0000332 M1 Human ErbB2 (clone 191924) Erb2 3B5 Abcam ab16901-100 NGAL (039-32-1) Ngal H-130 Santa Cruz sc-50350 Anti-iC3b (neoAntigen) Mab (013III-1.16) IC3b 013III-1.16 Thermo MA1-82814 Scientific Anti prostate-specific membrane antibody PSMA LN1-17 Biolegend 342502 Anti Cluster of differentiation 276 antibody B7H3 MIH35 Biolegend 135604 Anti-MART-1 (melanoma antigen recognized by MART1 3H2637 US Biologicals M2410 T-cells 1, Melan-A) Anti-MFG-E8 MFGE8 278918 R&D Systems MAB27671 Anti Vascular endothelial growth factor A VEGF A 5J63 US Biologicals V2110-05D antibody (5J63) Anti tumor microenvironment of metastasis 211 TMEM211 C-15 Santa Cruz sc-86534 antibody (c15) Anti-CA125 Cancer Antigen (Ovarian Cancer) CA125 9A101 US Biologicals C0050-01F DLL4 antibody DLL4 Abcam ab61031 Anti tissue inhibitor of metallo proteinase 1 TIMP1 63515 R&D Systems MAB970 antibody (63515) Anti-cluster of differentiation 10 antibody CD10(BD) HI10a BD Biosciences 555373 (HI10a) Human osteopontin Mab (clone 223112) OPN (SC) 223112 R&D Systems MAB14332 Anti Vascular endothelial growth factor receptor VEGFR2 89106 R&D Systems MAB3572 2 antibody (89106) Calmodulin antibody (J4D8) Calmodulin J4D8 Abcam ab75207 Estrogen receptor alpha antibody ER(1) ESR1α 33 Abcam ab2746 Estrogen receptor beta antibody (14C8) ER(2) ESR2β 14C8 Abcam ab288 APP/β-amyloid (NAB228) mouse Mab #2450 APP (β- NAB228 Cell Signaling 2450S amyloid) HSP-27 (G31) Mouse mAb #2402 HSP-27 G31 Cell Signaling 2402S Baculoviral IAP repeat containing protein 2 BIRC2 polyclonal Genway 18-464- 436439 Anti human proliferation associated 2G4 PA2G4 PA2GF5F8 Genway 18-464- 435060 SCGB2A2 mouse anti-human Mab Mammaglobin C-16 Santa Cruz sc-48328 Anti-plakoglobin (clone 15F11) junction 15F11 Millipore 04-139 plakoglobin Serotonin receptor 2B 5HT2B 4A9 Novus H00003357- M02 AGER/RAGE (1D1) AGER/RAGE 1D1 Novus H00000177- M05 RAGE RAGE polyclonal R&D Systems AF1145 C1GALT1 C1GALT1 1F1 Novus H00056913- M01 Derlin 1 antibody (1B9) derlin 1 1B9 Novus H000079139- M01 ErbB 3 antibody (RTJ2) HER3 RTJ2 Novus NB100-2691 Ornithine decarboxylase antibody (2G5) ODC1 2G5 Novus H00004953- M01 Syntaxin binding protein 4 antibody (EP302Y) STXBP4 EP302Y Novus NB110- 57608 Human 5T4 Mab (clone 524731) 5T4 524731 R&D Systems MAB4975 (trophoblast) Human ADAM10 Ectodomain Mab (clone ADAM10 163003 R&D Systems MAB1427 163003) Human/Mouse BACE-1 Mab (cline 137612) BACE1 137612 R&D Systems MAB931 Human BAI3 Mab (clone 409633) BAI3 409633 R&D Systems MAB39651 BRCA1 BRCA R&D Systems AF2210 Human BDNF Mab (clone 35909) BDNF 35909 R&D Systems MAB2481 Human IL-7 alpha/CD127 Mab (clone 40131) CD127 (IL7R) 40131 R&D Systems MAB306 Human CD44H Mab (clone 2C5)) CD44 2C5 R&D Systems BBA10 Anti-carcinoembryonic antigen (CEA, CD66e) CEA 487609 R&D Systems MAB41281 Human CXCR3 Mab (clone 49801) CXCR3 49801 R&D Systems MAB160 Anti-monocyte chemoattractant antibody MCP-1 CCL2 (MCP-1) S101 Genetex GTX18678 (S101) Anti tissue inhibitor of metallo proteinase 2 TIMP2 F27P3A4 Genetex GTX48556 antibody (F27P3A) Anti progesterone R antibody (clone H5344) PR(B) H5344 R&D Systems PP-H5344- 00 Anti Migration inhibitory factor-related protein 8 MRP8 8L802 US Biologicals M4688-36A Anti-Prostate Cell Surface antibody PCSA In house PCSA1058 Anti Mucin 1, cell surface associated protein Ab Muc1 VU4H5 Santa Cruz sc-7313 (Vu4H5) Anti Prostate specific antibody PSA 8A6 Santa Cruz sc-52173 Anit-cluster of differentiation 24 antibody (clone CD24 ml5 BD Biosciences 555426 ml5) Anti osteoprotegerin antibody OPG OPG-13 Biovendor RD182003110- 13 Anti unc 3 homolog A antibody (C13) UNC93a C-13 Santa Cruz sc-135539 Anti-adenomatous polyposis coli (clone 4i295) APC 4i295 US Biologicals A2298-70A Anti-Lewis y (CD174) (clone 8.S.289) CD174 8.S.289 US Biologicals L2056 Human 5′-Nucleotidase/CD73 (clone 606112) NT5E (CD73) 606112 R&D Systems MAB5795 Anti Epithelial cellular adhesion molecule EpCaM 158206 R&D Systems MAB9601 antibody Anti Mucin 17, cell surface associated protein MUC17 N-19 Santa Cruz sc-32600 Ab (n19) Anti tumor protein 53 antibody (DO-7) p53 DO7 Biolegend 645802 Anti Mucin 2, cell surface associated protein Ab MUC2 [996/1] Santa Cruz sc-15334 (H-300) Anti-hNCAM/CD56 antibody (301040) NCAM 301040 R&D Systems MAB2408 Anti-Aspartyl/asparaginyl β-hydroxylase (A10) ASPH (A-10) A10 Santa Cruz sc-271391 antibody Anti haptoglobin antibody (clone 1.C.1) HAP1 1.C.1 US Biologicals H1820-05 Anti tumor susceptibility gene 101 (Y16J) Tsg 101 Y16J Santa Cruz sc-101251 Human CXCR4 (Fusin) Mab (clone 44716) CXCR4 44716 R&D Systems MAB172 Human CXCR6 Mab (clone 56811) CXCR6 56811 R&D Systems MAB699 Human DPP6 Ectodomain Mab (clone 274311) DPP6 274311 R&D Systems MAB23601 Anti-ephrin-A receptor 2 antibody EphA2 233720 R&D Systems MAB639 Human ErbB4 Mab (clone 182818) Erb B4 182818 R&D Systems MAB11311 Human ErbB3 (clone 526806) erb3 (Erb-B3) 526806 R&D Systems MAB3483 Human Galectin-3 (clone 194805) Gal3 194805 R&D Systems MAB11541 Anti human GPER GPER (GPR30) R&D Systems AF5534 Human/Mouse HSP70/HSPA1A Mab (clone HSP70 242707 R&D Systems MAB1663 242707) Anti Matrixmetallo proteinase 9 antibody MMP9 36020 R&D Systems MAB936 Human NG2/MCSP Mab (clone LHM-2) NG2 (CSPG4) LHM-2 R&D Systems MAB2585 Human PARK7/DJ-1 (clone 421015) PARK7 421015 R&D Systems MAB3995 Human TRAIL R2/TNFRSF10B antibody TRAIL-R2 71903 R&D Systems MAB631 Human TrkB (clone 75133) TrkB 75133 R&D Systems MAB397 CRMP-2 (1B1) CRMP-2 1B1 Santa Cruz sc-101348 p-NHE-3 (10A8) NHE-3 10A8 Santa Cruz sc-53961 BACE (61-3E7) BACE1 61-3E7 Santa Cruz sc-33711 Anti-c-K-RAS antibody KRAS 234-4.2 Sigma R3400 Anti-SCL16A7 MCT2 Sigma SAB2500948 Monoclonal Anti TRAF-4 antibody (clone 3F6) TRAF4 3F6 Sigma WH0009618 (scaffolding) M1 Anti-Keratin 15 Mab (LHK15) Keratin 15 LHK15 Thermo MA1-38005 Scientific Anti-cytokeratin 19 fragment antibody CYFRA 21 5F230 US Biologicals C9097-28 CYFRA21-1 Anti-YB-1 YB-1 21A3 IBL-America 11106 Anti-YB-1 YB-1 VEGF Receptor 1 Antibody (4H5) VEGFR1 4H5 Novus H00002321- M01 pan Cadherin Antibody (CDH1) eCadherin 3F4 Novus H00000999- M01 eCadherin eCadherin 16A Chemicon MAB3199Z GCDPF-15 (prolactin-induced protein) GCDPF-15 D6 Enzo Life ALX-801- Sciences 051-L001 TGFB1-induced protein BigH3 K1H12 Acris AM09047PU-N BCA-200 hybridoma ATCC number HB-8696 BCA-200 520C9 in house

Methods are presented in Examples 49-50 above. Briefly, the capture antibodies were conjugated to microbeads. The beads were subjected to quality control testing using appropriate anti-species antibodies to confirm that the capture antibodies were efficiently bound to the beads. Antibody-conjugated beads were analyzed for binding to cMVs from the breast cancer cell line MCF7 that had been activated by 60 min incubation at 37° C. in normal (i.e., non-cancer) female plasma. The beads were next incubated with PE labeled detector antibodies. The fluorescence levels the beads was determined for a standard curve (titration of cMVs/ml plasma) and compared to the fluorescence level of beads in control (i.e., normal, non-cancer) plasma that had not been spiked with MCF7 cMVs. Table 85 shows the fold change increase in the samples containing the MCF7 cMVs compared to the controls.

In a related set of experiments, the above antibody-conjugated beads were used to detect cMVs in breast cancer patient plasma. cMV levels were compared between five late stage breast (stage II/III) invasive ductal cancer plasma and five age compatible female plasma controls. The controls were from women that were self-identified as non-cancer. Table 85 also shows the fold change increase in the samples from the breast cancer patients compared to the non-cancer controls.

TABLE 85 Increased Levels of cMV Antigens in Breast Cancer Samples Fold increase Antigen name Fold increase MCF7 in plasma 5T4 (trophoblast) 27.3 8.25 ADAM10 100.4 20.9 AGER/RAGE 24.2 11.97 APC 27.4 9.84 APP (β-amyloid) 27.4 7.48 ASPH (A-10) 6.2 1.93 B7H3 (CD276) 34 5.85 BACE1 6.8 10.51 BAI3 24.8 6.19 BRCA1 221.4 7.53 BDNF 13.2 21.42 BIRC2 22.2 10.36 C1GALT1 34.3 15.75 CA125 (MUC16) 11.8 6.8 Calmodulin 1 119.4 9.88 CCL2 (MCP-1) 20.8 15.67 CD9 37.3 4.77 CD10(BD) 21.4 3.45 CD127 (IL7R) 19.2 12.88 CD174 285.4 3.59 CD24 38.1 16.68 CD44 23.1 7.19 CD63 12 1.66 CD81 11.7 1.31 CEA 25.9 11.43 CRMP-2 8.7 2.14 CXCR3 18.1 9.84 CXCR4 2.9 0.94 CXCR6 55.5 8.84 CYFRA 21 8.7 2.04 derlin 1 14.7 17.58 DLL4 24 10.17 DPP6 52 8.19 E-CAD 6.4 1.59 EpCAM 69.7 7.61 EphA2 (H-77) 50 7.17 ER(1) ESR1α 19.4 5.49 ER(2) ESR2β 30.3 6.35 Erb B4 53.4 6.57 Erbb2 14.3 11.97 erb3 (Erb-B3) PA2G4 31.5 5.65 Gal3 47.7 6.05 GPR30 (G-coupled ER1) 43.6 5.59 HAP1 10.2 1.92 HER3 52.8 10.03 HSP-27 14.1 5.4 HSP70 151.6 12.97 IC3b 7.2 2.19 IL8 33.7 8.61 junction plakoglobin 14.5 9.21 Keratin 15 4.7 2.17 Mammaglobin 6.1 1.72 MART1 1.3 1.74 MCT2 38 6.39 MFGE8 as detector Ab-PE 36.9 4.74 MMP9 29.7 15.79 MRP8 2.3 14.71 Muc1 8.2 1.95 MUC17 6.6 1.93 MUC2 3.5 1.84 NCAM 10 6.76 NG2 (CSPG4) 100.8 16.9 Ngal 79.1 8.9 NHE-3 7.6 1.85 NT5E (CD73) 43.5 16.89 ODC1 13.5 7.05 OPG 12.5 2.23 OPN (SC) 9.5 9.39 p53 14.8 7.3 PARK7 51.6 7.81 PCSA 51.3 7.81 PGP9.5 (PARK5) 27.6 10.49 PR(B) 2.4 2 PSA 6.7 1.79 PSMA 30 7.74 RAGE 48.2 7.2 STXBP4 20 12.61 Survivin 29.8 12.8 TFF3 (secreted) 2.3 1.61 TIMP1 27.1 7.53 TIMP2 56.1 5.91 TMEM211 157.2 5.72 TRAF4 (scaffolding) 22.3 9.93 TRAIL-R2 (death Receptor 5) 61.5 9.48 TrkB 22.8 5.54 Tsg 101 0.5 0.92 UNC93a 7.5 1.97 VEGF A 10.1 11.32 VEGFR2 3.9 8.66 YB-1 5.4 2.19 VEGFR1 0.9 1.19 5HT2B (serotonin receptor 2B) 14.6 2.59 BRCA1 221.4 7.53 BACE 1 8.3 2.04 CDH1-cadherin 4.5 1.75

Example 81 Identifying Tumors of Breast Cancer Origin

In this Example, a biosignature is created to identify the origin of a Cancer of Unknown Primary (CUPS). Such a biosignature can be used to characterize the origin of a metastasized tumor and thereby shed light on potential treatment options. In the Example, markers were identified using tumor tissue. The markers can be further validated as part of a circulating biomarker signature, e.g., a biosignature comprising cMVs.

Thirty whole genome microarray datasets from breast tumor samples were compared to thirty microarray datasets from a panel of other tumor samples. The expression of informative genes was compared using various statistical modeling approaches to identify biosignatures for differentiating the breast cancer samples. Microarray data was obtained using the Illumina Whole Genome DASL Assay with UDG (Illumina, cat# DA-903-1024/DA-903-1096) although data from many appropriate expression systems can be used.

Classification and Regression Tree (CART).

In a first approach, a Classification and Regression Tree (CART) approach was used to identify a breast cancer profile. See generally Breiman, Leo; Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Monterey, Calif.: Wadsworth & Brooks/Cole Advanced Books & Software. All gene expression data was used. A five-fold cross-validation procedure was used to determine optimal genes for differentiating breast cancer from the other tumor types as follows:

-   -   (a) Randomly partition the data into five training sets and five         test sets     -   (b) The proportion of breast cancer and other tumor types (i.e.,         50/50) was maintained across partitions     -   (c) Each partition used 80% of the data for training a         classifier for breast cancer and the remaining 20% was used to         test the classifier performance     -   (d) The partitions are constructed so that each sample is         included in at least on test set

The results of the CART cross-validation across all partitions are shown in Table 86. Detection of breast cancer samples was considered as positive. Using this approach, an accuracy of 96% was obtained. The specificity was 100% (i.e., no other cancers were incorrectly called positive for breast cancer) and the sensitivity for detecting breast cancer was 95% (i.e., all but two breast cancers were detected as breast cancer). A single transcript was identified in each partition that produced these results. In one partition, the gene was AK5.2. In another partition, the gene was ATP6V1B1. In the three other partitions, the gene was CRABP1.

TABLE 86 Breast Cancer Profile using CART Cross-Validation Breast Cancer - True Other Cancer - True Accuracy Breast Cancer - 28 0 28 Predicted Other Cancer - 2 30 32 Predicted Total 30 30 96%

Generalized Lasso Regression.

Generalized lasso regression models attempt to find the fewest independent, linear predictors for a binary outcome variable. See Roth, The generalized LASSO, IEEE Trans Neural Netw. 2004 January; 15(1):16-28.

The results of the generalized lasso regression are shown in Table 87. Detection of breast cancer samples was considered as positive. Using this approach, an accuracy of 95% was obtained. The specificity was 100% (i.e., no other cancers were incorrectly called positive for breast cancer) and the sensitivity for detecting breast cancer was 95% (i.e., all but three breast cancers were detected as breast cancer). Three transcripts were identified to build this classifier: DST.3, GATA3 and KRT81. The results are shown graphically in FIG. 126A. In the figure, the upper tree includes the 27 correctly identified breast cancer samples and the lower tree includes the 30 correctly identified non-breast cancer samples in addition to the 3 incorrectly classified breast cancers. Note that the three incorrectly identified breast cancer samples are in the small cluster directly adjacent to the correctly classified breast cancers.

TABLE 87 Breast Cancer Profile using Generalized Lasso Regression Breast Cancer - True Other Cancer - True Accuracy Breast Cancer - 27 0 27 Predicted Other Cancer - 3 30 33 Predicted Total 30 30 95%

Bayesian Ensemble.

A Bayesian classifier was constructed. The Bayesian Ensemble approach is capable of detecting non-linear relationships. See, e.g., Mitchell, Machine Learning, 1997, pp. 175; Hoeting et al (1999). “Bayesian Model Averaging: A Tutorial”. Statistical Science 14 (4): 382-401; Haussler et al. Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension. Machine Learning, 14:83-113, 1994; Domingos (2000). “Bayesian averaging of classifiers and the overfilling problem”. Proceedings of the 17th International Conference on Machine Learning (ICML). pp. 223-230.

In this approach, expression levels were assigned to one of four bins based on fluorescence intensity values. All combinations of two gene expression values and single gene expression values were evaluated for their ability to distinguish between the breast cancer and other cancer samples. The gene expression values were analyzed using a Bayesian scoring function. This approach can identify non-linear relationships between the gene expression and the type of tumor. Thousands of fragments are analyzed to generate a weighted collection of likely classifiers. Cross Validation as described above was used to assess the performance of the classifiers. The resulting genes are those that define the fragments in the ensemble.

The results of the Bayesian Ensemble are shown in Table 88. Detection of breast cancer samples was considered as positive. Using this approach, an accuracy of 97% was obtained. The specificity was 100% (i.e., no other cancers were incorrectly called positive for breast cancer) and the sensitivity for detecting breast cancer was 97% (i.e., all but one breast cancer was detected as breast cancer). Fifteen transcripts were identified to build this classifier: AK5.2, ATP6V1B1, CRABP1, DST.3, ELF5, GATA3, KRT81, LALBA, OXTR, RASL10A, SERHL, TFAP2A.1, TFAP2A.3, TFAP2C and VTCN1. The results are shown graphically in FIG. 126B. In the figure, the upper tree includes the 29 correctly identified breast cancer samples and the lower tree includes the 30 correctly identified non-breast cancer samples in addition to the one incorrectly classified breast cancer.

TABLE 88 Breast Cancer Profile using Cross-Validation of Bayesian Ensemble Approach Breast Cancer - True Other Cancer - True Accuracy Breast Cancer - 29 0 29 Predicted Other Cancer - 1 30 31 Predicted Total 30 30 97%

The Bayesian Ensemble approach provides a confidence metric for its predictions. This metric can be used to aid pathologists or other care givers that are classifying the sample. For example, if the pathologist classifies a sample as non-breast whereas the algorithm classifies the sample as breast with a specified confidence threshold, the sample can be flagged for repeated examination and confirmation. Similarly, if the pathologist classifies a sample as breast whereas the algorithm classifies the sample as non-breast with a specified confidence threshold, the sample can be flagged for repeated examination and confirmation. The confidence threshold can be adjusted depending on requirements such as the magnitude of error for misclassifying the sample. For example, the threshold can be 70, 80 or 90%.

Adaption of the biosignature to a vesicle test will allow non-invasive determination of the origin of a cancer of unknown primary.

Example 82 Circulating Protein Biomarkers of Breast Cancer Origin

In this Example, cMV were queried using antibody arrays to identify a cMV protein signature that distinguishes between healthy controls, breast cancer patients (non-DCIS) and ductal carcinoma insitu (DCIS) patients. The sample set comprised plasma-derived cMVs from nine breast cancer patients, four DCIS patients and eight self-declared normal subjects, as well as cMVs from MCF3, MDA MB231 and T47D cell lines. The samples were incubated on a Full Moon BioSystems 649 antibody array (Full Moon BioSystems, Inc., Sunnyvale, Calif.) according to the manufacturer's instructions. Arrays were scanned on an Agilent scanner and data from images was extracted using Feature Extractor software (Agilent Technologies, Inc., Santa Clara, Calif.). Extracted data was normalized to array negative controls and normalized fluorescent values were analyzed with GeneSpring GX software (Agilent). 113 proteins were found to have a greater than 1.3 fold change difference in breast cancer samples compared to controls. See Table 89. 86 proteins were found to have a 2.0 fold change or greater in DCIS samples compared to controls. See Table 90. 23 proteins were found to have a 2 fold or greater change between DCIS and cancers. See Table 91.

TABLE 89 Biomarkers found to have a 1.3 fold or greater difference between plasma-derived cMV from healthy controls and breast cancer patients Fold change Regulation (in cancer vs (in cancer vs Biomarker control) control) TRAP 1.64 up Renal Cell Carcinoma 1.63 up Filamin 1.53 up 14.3.3, Pan 1.53 up Prohibitin 1.50 up c-fos 1.41 up Ang-2 1.40 up GSTmu 1.40 up Ang-1 1.39 up FHIT 1.39 up Rad51 1.38 up Inhibin alpha 1.37 up Cadherin-P 1.35 up 14.3.3 gamma 1.32 up p18INK4c 1.31 up P504S 1.31 up XRCC2 1.30 up Caspase 5 1.30 up CREB-Binding Protein 1.30 up Estrogen Receptor −1.30 down IL17 −1.30 down Claudin 2 −1.30 down Keratin 8 −1.31 down GAPDH −1.31 down CD1 −1.31 down Keratin, LMW −1.31 down Gamma Glutamylcysteine −1.31 down Synthetase (GCS)/Glutamate-cysteine Ligase a-B-Crystallin −1.31 down Pax-5 −1.32 down MMP-19 −1.32 down APC −1.32 down IL-3 −1.32 down Keratin 8 (phospho-specific Ser73) −1.32 down TGF-beta 2 −1.33 down ITK −1.33 down Oct-2/ −1.33 down DJ-1 −1.33 down B7-H2 −1.33 down Plasma Cell Marker −1.33 down Rad18 −1.33 down Estriol −1.33 down Chk1 −1.33 down Prolactin Receptor −1.34 down Laminin Receptor −1.34 down Histone H1 −1.34 down CD45RO −1.34 down GnRH Receptor −1.34 down IP10/CRG2 −1.35 down Actin, Muscle Specific −1.35 down S100 −1.35 down Dystrophin −1.35 down Tubulin-a −1.36 down CD3zeta −1.36 down CDC37 −1.36 down GABA a Receptor 1 −1.36 down MMP-7 (Matrilysin) −1.37 down Heregulin −1.37 down Caspase 3 −1.38 down CD56/NCAM-1 −1.38 down Gastrin 1 −1.38 down SREBP-1 (Sterol Regulatory Element −1.38 down Binding Protein-1) MLH1 −1.38 down PGP9.5 −1.39 down Factor VIII Related Antigen −1.39 down ADP-ribosylation Factor (ARF-6) −1.39 down MHC II (HLA-DR) Ia −1.40 down Survivin −1.40 down CD23 −1.40 down G-CSF −1.40 down CD2 −1.41 down Calretinin −1.41 down Neuron Specific Enolase −1.41 down CD165 −1.41 down Calponin −1.41 down CD95/Fas −1.42 down Urocortin −1.42 down Heat Shock Protein 27/hsp27 −1.42 down Topo II beta −1.42 down Insulin Receptor −1.42 down Keratin 5/8 −1.42 down sm −1.43 down Actin, skeletal muscle −1.43 down CA19-9 −1.44 down GluR1 −1.45 down GRIP1 −1.45 down CD79a mb-1 −1.46 down TdT −1.46 down HRP −1.46 down CD94 −1.47 down CCK-8 −1.48 down Thymidine Phosphorylase −1.48 down CD57 −1.48 down Alkaline Phosphatase (AP) −1.49 down CD59/MACIF/MIRL/Protectin −1.49 down GLUT-1 −1.49 down alpha-1-antitrypsin −1.50 down Presenillin −1.50 down Mucin 3 (MUC3) −1.51 down pS2 −1.52 down 14-3-3 beta −1.53 down MMP-13 (Collagenase-3) −1.55 down Fli-1 −1.56 down mGluR5 −1.57 down Mast Cell Chymase −1.57 down Laminin B1/b1 −1.57 down Neurofilament (160 kDa) −1.59 down CNPase −1.61 down Amylin Peptide −1.63 down Gail −1.67 down CD6 −1.75 down alpha-1-antichymotrypsin −1.80 down E2F-2 −2.01 down MyoD1 −2.17 down

TABLE 90 Biomarkers with a 2 fold or greater difference between plasma-derived cMV from DCIS patients and healthy controls Fold change (in DCIS vs Regulation (in Biomarker control) DCIS vs control) Laminin B1/b1 −3.91 down E2F-2 −3.15 down TdT −3.13 down Apolipoprotein D −2.90 down Granulocyte −2.80 down Alkaline Phosphatase (AP) −2.78 down Heat Shock Protein 27/hsp27 −2.75 down CD95/Fas −2.74 down pS2 −2.63 down Estriol −2.63 down GLUT-1 −2.61 down Fibronectin −2.61 down CD6 −2.59 down CCK-8 −2.55 down sm −2.54 down Factor VIII Related Antigen −2.51 down CD57 −2.51 down Plasminogen −2.49 down CD71/Transferrin Receptor −2.46 down Keratin 5/8 −2.46 down Thymidine Phosphorylase −2.45 down CD45/T200/LCA −2.42 down Epithelial Specific Antigen −2.42 down Macrophage −2.38 down CD10 −2.38 down MyoD1 −2.38 down Gai1 −2.38 down bcl-XL −2.37 down hPL −2.35 down Caspase 3 −2.35 down Actin, skeletal muscle −2.33 down IP10/CRG2 −2.31 down GnRH Receptor −2.30 down p35nck5a −2.27 down ADP-ribosylation Factor (ARF-6) −2.26 down Cdk4 −2.26 down alpha-1-antitrypsin −2.25 down IL17 −2.25 down Neuron Specific Enolase −2.21 down CD56/NCAM-1 −2.18 down Prolactin Receptor −2.17 down Cdk7 −2.17 down CD79a mb-1 −2.17 down Collagen IV −2.16 down CD94 −2.15 down Myeloid Specific Marker −2.15 down Keratin 10 −2.15 down Pax-5 −2.14 down IgM (m-Heavy Chain) −2.14 down CD45RO −2.13 down CA19-9 −2.13 down Mucin 2 −2.12 down Glucagon −2.12 down Mast Cell Chymase −2.11 down MLH1 −2.11 down CD1 −2.11 down CNPase −2.10 down Parkin −2.09 down MHC II (HLA-DR) Ia −2.09 down B7-H2 −2.09 down Chk1 −2.09 down Lambda Light Chain −2.09 down MHC II (HLA-DP and DR) −2.08 down Myogenin −2.08 down MMP-7 (Matrilysin) −2.08 down Topo II beta −2.08 down CD53 −2.06 down Keratin 19 −2.06 down Rad18 −2.06 down Ret Oncoprotein −2.06 down MHC II (HLA-DP) −2.06 down E3-binding protein (ARM1) −2.06 down Progesterone Receptor −2.05 down Keratin 8 −2.05 down IgG −2.05 down IgA −2.04 down Tubulin −2.04 down Insulin Receptor Substrate-1 −2.03 down Keratin 15 −2.03 down DR3 −2.02 down IL-3 −2.02 down Keratin 10/13 −2.02 down Cyclin D3 −2.02 down MHC I (HLA25 and HLA-Aw32) −2.00 down Calmodulin −2.00 down Neurofilament (160 kDa) −2.00 down

TABLE 91 Proteins found to have a two fold or greater difference between plasma-derived cMV from DCIS patients and breast cancer patients Fold change (in DCIS vs Regulation (in Name cancer) DCIS vs cancer) Macrophage −2.54 down Fibronectin −2.02 down Granulocyte −3.04 down Keratin 19 −2.11 down Cyclin D3 −2.07 down CD45/T200/LCA −2.21 down EGFR −2.01 down Thrombospondin −2.12 down CD81/TAPA-1 −2.04 down Ruv C −2.02 down Plasminogen −2.03 down Collagen IV −2.08 down Laminin B1/b1 −2.49 down CD10 −2.00 down TdT −2.15 down Filamin −2.61 down bcl-XL −2.00 down 14.3.3 gamma −2.27 down 14.3.3, Pan −2.42 down p170 −2.08 down Apolipoprotein D −2.69 down CD71/Transferrin Receptor −2.21 down FHIT −2.32 down

The cMV markers in Tables 89-91 can be used in a biosignature to differentiate normal versus breast cancer patients (Table 89), normal versus DCIS patients (Table 90), and DCIS versus non-DCIS breast cancers (Table 91).

Example 83 Circulating Protein Biomarkers of Lung Cancer Origin

In this Example, cMV were queried using antibody arrays to identify a cMV protein signature that distinguishes between healthy controls and lung cancer patients. The sample set comprised plasma-derived cMVs from 10 non-small cell lung cancer (NSCLC) patients (five Stage I, three Stage II, two Stage III) and 10 self-declared normal subjects. The samples were incubated on a Full Moon BioSystems 649 antibody array (Full Moon BioSystems, Inc., Sunnyvale, Calif.) according to the manufacturer's instructions. Arrays were scanned on an Agilent scanner and data from images was extracted using Feature Extractor software (Agilent Technologies, Inc., Santa Clara, Calif.). Extracted data was normalized to array negative controls and normalized fluorescent values were analyzed with GeneSpring GX software (Agilent). 166 proteins were found to have a greater than 1.5-fold change difference in breast cancer samples compared to controls. See Table 92. In the tables, “raw” refers to the normalized fluorescent values.

TABLE 92 Biomarkers found to have a 1.5 fold or greater difference between plasma-derived cMV from healthy controls and lung cancer patients Regulation Fold change (in lung (in lung cancer cancer vs Biomarker vs normal) normal) Prohibitin 3.31 up CD23 2.33 up Amylin Peptide 2.25 up HRP 2.20 up Rad51 2.17 up Pax-5 2.11 up Oct-3/ 2.07 up GLUT-1 2.01 up PSCA 2.00 up Thrombospondin 1.96 up FHIT 1.95 up a-B-Crystallin 1.88 up LewisA 1.87 up Vacular Endothelial Growth Factor(VEGF) 1.87 up Hepatocyte Factor Homologue-4 1.83 up Flt-4 1.82 up GluR6/7 1.81 up Prostate Apoptosis Response Protein-4 1.79 up GluR1 1.79 up Fli-1 1.77 up Urocortin 1.74 up S100A4 1.74 up 14-3-3 beta 1.73 up P504S 1.73 up HDAC1 1.72 up PGP9.5 1.72 up DJ-1 1.72 up COX2 1.71 up MMP-19 1.71 up Actin, skeletal muscle 1.70 up Claudin 3 1.70 up Cadherin-P 1.69 up Collagen IX 1.67 up p27Kip1 1.67 up Cathepsin D 1.66 up CD30 (Reed-Sternberg Cell Marker) 1.66 up Ubiquitin 1.65 up FSH-b 1.63 up TrxR2 1.63 up CCK-8 1.60 up Cyclin C 1.60 up CD138 1.59 up TGF-beta 2 1.59 up Adrenocorticotrophic Hormone 1.58 up PPAR-gamma 1.57 up Bcl-6 1.57 up GLUT-3 1.56 up IGF-I 1.55 up mRANKL 1.55 up Fas-ligand 1.55 up Filamin 1.55 up Calretinin 1.55 up Oct-1 1.54 up Parathyroid Hormone 1.54 up Claudin 5 1.53 up Claudin 4 1.53 up Raf-1 (Phospho-specific) 1.53 up CDC14A Phosphatase 1.53 up Mitochondria 1.52 up APC 1.52 up Gastrin 1 1.51 up Ku (p80) −1.50 down Gai1 −1.50 down XPA −1.51 down Maltose Binding Protein −1.52 down Melanoma (gp100) −1.52 down Phosphotyrosine −1.52 down Amyloid A −1.52 down CXCR4/Fusin −1.53 down Hepatic Nuclear Factor-3B −1.53 down Caspase 1 −1.53 down HPV 16-E7 −1.53 down Axonal Growth Cones −1.53 down Lck −1.54 down Ornithine Decarboxylase −1.54 down Gamma Glutamylcysteine −1.54 down Synthetase(GCS)/Glutamate-cysteine Ligase ERCC1 −1.54 down Calmodulin −1.54 down Caspase 7 (Mch 3) −1.55 down CD137 (4-1BB) −1.56 down Nitric Oxide Synthase, brain (bNOS) −1.56 down E2F-2 −1.56 down IL-10R −1.56 down L-Plastin −1.57 down CD18 −1.58 down Vimentin −1.58 down CD50/ICAM-3 −1.59 down Superoxide Dismutase −1.60 down Adenovirus Type 5 E1A −1.60 down PHAS-I −1.61 down Progesterone Receptor (phospho-specific)- −1.61 down Serine 294 MHC II (HLA-DQ) −1.61 down XPG −1.62 down ER Ca+2 ATPase2 −1.62 down Laminin-s −1.62 down E3-binding protein (ARM1) −1.62 down CD45RO −1.63 down CD1 −1.63 down Cdk2 −1.63 down MMP-10 (Stromilysin-2) −1.64 down sm −1.64 down Surfactant Protein B (Pro) −1.64 down Apolipoprotein D −1.64 down CD46 −1.65 down Keratin 8 (phospho-specific Ser73) −1.66 down PCNA −1.66 down PLAP −1.67 down CD20 −1.68 down Syk −1.69 down LH −1.69 down Keratin 19 −1.70 down ADP-ribosylation Factor (ARF-6) −1.70 down Int-2 Oncoprotein −1.71 down Luciferase −1.74 down AIF (Apoptosis Inducing Factor) −1.74 down Grb2 −1.75 down bcl-X −1.75 down CD16 −1.75 down Paxillin −1.75 down MHC II (HLA-DP and DR) −1.75 down B-Cell −1.76 down p21WAF1 −1.78 down MHC II (HLA-DR) −1.78 down Tyrosinase −1.79 down E2F-1 −1.79 down Pds1 −1.80 down Calponin −1.80 down Notch −1.81 down CD26/DPP IV −1.81 down SV40 Large T Antigen −1.81 down Ku (p70/p80) −1.82 down Perforin −1.84 down XPF −1.84 down SIM Ag (SIMA-4D3) −1.85 down Cdk1/p34cdc2 −1.86 down Neuron Specific Enolase −1.87 down b-2-Microglobulin −1.89 down DNA Polymerase Beta −1.90 down Thyroid Hormone Receptor, Human −1.90 down Alkaline Phosphatase (AP) −1.91 down Plasma Cell Marker −1.92 down Heat Shock Protein 70/hsp70 −1.92 down TRP75/gp75 −1.92 down SRF (Serum Response Factor) −1.93 down Laminin B1/b1 −1.97 down Mast Cell Chymase −1.98 down Caldesmon −1.99 down CEA/CD66e −2.00 down CD24 −2.01 down Retinoid X Receptor (hRXR) −2.08 down CD45/T200/LCA −2.08 down Rabies Virus −2.12 down Cytochrome c −2.12 down DR3 −2.16 down bcl-XL −2.23 down Fascin −2.46 down CD71/Transferrin Receptor −2.61 down

One or more of the cMV markers in Table 92 can be used in a biosignature to differentiate normal versus lung cancer patients.

Although preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby. 

1. A method of detecting biomarkers in a biological sample comprising: (a) contacting a biological sample with reagents designed to specifically recognize Gal3 and BCA200; and (b) identifying the Gal3 and BCA200 in the biological sample in contact with the reagents, thereby detecting the biomarkers in the biological sample.
 2. The method of claim 1, wherein the biological sample comprises a biological fluid.
 3. The method of claim 2, wherein the biological fluid comprises peripheral blood, sera, plasma, ascites, urine, cerebrospinal fluid (CSF), sputum, saliva, bone marrow, synovial fluid, aqueous humor, amniotic fluid, cerumen, breast milk, broncheoalveolar lavage fluid, semen, prostatic fluid, cowper's fluid or pre-ejaculatory fluid, female ejaculate, sweat, fecal matter, hair, tears, cyst fluid, pleural and peritoneal fluid, pericardial fluid, lymph, chyme, chyle, bile, interstitial fluid, menses, pus, sebum, vomit, vaginal secretions, mucosal secretion, stool water, pancreatic juice, lavage fluids from sinus cavities, bronchopulmonary aspirates, blastocyl cavity fluid, or umbilical cord blood.
 4. The method of claim 2, wherein the biological fluid comprises blood or a blood derivative.
 5. The method of claim 1, wherein the Gal3 and BCA200 comprise surface antigens of extracellular microvesicles.
 6. (canceled)
 7. (canceled)
 8. The method of claim 5, wherein the extracellular microvesicles are isolated from the biological sample prior to the identifying step.
 9. The method of claim 8, wherein the isolation comprises size exclusion chromatography, density gradient centrifugation, differential centrifugation, nanomembrane ultrafiltration, immunoabsorbent capture, affinity selection, affinity purification, affinity capture, immunoassay, immunoprecipitation, microfluidic separation, flow cytometry or combinations thereof.
 10. The method of claim 9, wherein the affinity selection comprises contacting the extracellular microvesicles with the reagents.
 11. The method of claim 10, wherein the reagents comprises a nucleic acid, DNA molecule, RNA molecule, antibody, antibody fragment, aptamer, peptoid, zDNA, peptide nucleic acid (PNA), locked nucleic acid (LNA), lectin, peptide, dendrimer, membrane protein labeling agent, chemical compound, or a combination thereof.
 12. The method of claim 10, wherein the reagents are used to capture and/or detect the extracellular microvesicles.
 13. (canceled)
 14. The method of claim 12, wherein the reagents are bound to a substrate.
 15. (canceled)
 16. The method of claim 12, wherein reagents carry a label.
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 26. The method of claim 1, further comprising contacting the biological sample with reagents that specifically recognize at least one of OPN, and NCAM.
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 35. The method of claim 1, further comprising contacting the biological sample with reagents that specifically recognize at least one biomarker in any of Tables 89-92.
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 43. The method of claim 5, wherein the extracellular microvesicles are captured with the reagents and are further detected with at least one binding agent to a biomarker that is selected from the group consisting of a tetraspanin, CD9, CD31, CD63, CD81, CD82, CD37, CD53, Rab-5b, Annexin V, MFG E8, and a combination thereof.
 44. The method of claim 5, further comprising detecting the level of a payload within the extracellular microvesicles.
 45. The method of claim 44, wherein the detected payload comprises at least one nucleic acid, peptide, protein, lipid, antigen, carbohydrate, or proteoglycan.
 46. (canceled)
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 48. The method of claim 45, wherein the nucleic acid comprises at least one DNA, mRNA, microRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, or shRNA.
 49. (canceled)
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 54. The method of claim 1, wherein the biological sample comprises a cancer cell culture or a sample from a subject having or suspected of having a cancer.
 55. The method of claim 54, further comprising comparing the presence or level of the detected extracellular microvesicles to a reference, wherein an altered presence or level relative to the reference provides a diagnostic, prognostic, or theranostic determination for the cancer.
 56. (canceled)
 57. (canceled)
 58. The method of claim 55, wherein the reference is from a biological sample without the cancer.
 59. The method of claim 58, wherein elevated levels of the one or more biomarker in the sample as compared to the reference indicates the presence of or the likelihood of the cancer in the sample, or the presence of or the likelihood of a more advanced cancer in the sample.
 60. (canceled)
 61. (canceled)
 62. The method of claim 54, wherein the cancer comprises breast cancer.
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